METHOD AND APPARATUS FOR MEASURING HEART-RELATED PARAMETERS AND DERIVING HUMAN STATUS PARAMETERS FROM SENSED PHYSIOLOGICAL AND CONTEXTUAL PARAMETERS
A monitor device and associated methodology are disclosed which provide a self contained, relatively small and continuously wearable package for the monitoring of heart related parameters, including ECG. The detection of heart related parameters is predicated on the location of inequipotential signals located within regions of the human body conventionally defined as equivalent for the purpose of detection of heart related electrical activity, such as on single limbs. Amplification, filtering and processing methods and apparatus are described in conjunction with analytical tools for beat detection and display.
This application is a continuation of co-pending U.S. application Ser. No. 10/940,889 entitled Method and Apparatus for Measuring Heart Related Parameters filed Sep. 13, 2004, which claims the benefit of U.S. Provisional Application Ser. No. 60/502,764, filed Sep. 12, 2003; U.S. Provisional Application Ser. No. 60/510,013, filed Oct. 9, 2003; and U.S. Provisional Application Ser. No. 60/555,280, filed Mar. 22, 2004.
BACKGROUND1. Field
The present invention relates to a method and apparatus for accurately measuring heart related parameters from within a conventionally defined equivalence region of the human body. More particularly, a method and apparatus is disclosed for measuring an ECG signal and other heart related parameters such as heart beats or heart rate from a single limb of the human body. Most specifically, the heart related parameters are taken from the upper left or right arm.
2. Description of the Related Art
The heart is a muscular pump that is controlled by a natural electrical system that causes the heart muscle to contract and pump blood through the heart to the lungs and the rest of the body, carrying oxygen as well as other needed nutrients. The heart can be characterized by a set of parameters that describe the state of the heart, including the frequency and timing of the contractions of the four chambers of the heart, and the pattern of electrical signals causing those contractions. There are many methods of detecting these parameters that are well known in the art, including: sensing the electrical impulses of the heart, sensing the pulse of blood as it moves through arteries, Doppler and other acoustic based methods, capacitance, micro-impulse radar, pressure- and/or motion-based methods such as by utilizing piezo-electric elements or strain gauges, and optical methods in areas where the pulsing of blood can be externally viewed, such as in a pulse-oximeter.
The most well-known and conventional method utilized today for measuring heart-related parameters is the electrocardiogram. An electrocardiogram, or ECG, signal is a surface measurement of the electrical potential of the heart generated by electrical activity in cardiac tissue. This measurement can be made using electrodes placed on the surface of the skin because the entire body is capable of conducting electricity.
ECG measurements may be used to provide information about a number of heart related parameters, including, but not limited to, the heart beat rate, or heart rate, for a number of applications, such as medical diagnostic, health awareness and sports performance applications. The most reliable heart rate calculation based upon ECG is performed by detecting each QRS complex, and thus each heart beat, because the QRS complex contains the highest amount of energy and its spectrum differs sufficiently from the spectrum of movement artifacts. Beats are typically counted at each R point (the peak), and the distance between a first R point and a subsequent R point is known as the R-R interval, which, when inverted, yields the instantaneous heart rate. Other parameters such as the heart-rate variability are also computable from the set of R-R intervals.
As discussed above, the heart is a source of a voltage potential difference resulting from the electrical activity that causes the heart muscles to contract. This potential difference is known in the art as the heart's action potential. An ECG signal is a measurement of this action potential. In addition, the heart is positioned in the left chest area and is oriented at an angle slightly off of vertical. The traditional model of ECG measurement indicates that ECG measurements must be taken across the heart, meaning using electrodes placed on either side of an imaginary line running through the center of the heart. Many different researchers have identified the various sections of the surface of the body in different ways with respect to placing electrodes for measuring different aspects of the heart's electrical activity.
Generally, these placements are identified in two ways. First, pairs of electrodes are often used to measure the electrical potential difference between two points. If two points show an electrical potential signal that varies with the activity of the heart they are said to be not equipotential or therefore inequipotential with respect to one another. Inequipotential therefore refers solely to the difference in the heart's action potential rather than other sources of voltage difference such as EMG. Furthermore, locations are described herein as measuring a different aspect of the heart's electrical activity from other locations when those two locations are inequipotential. The electrodes are conventionally placed in a manner as to obtain maximum differentiation between the electrodes. Conventionally, therefore, the body is divided into quadrants I, II, III and IV, as illustrated in
Other models include the Einthoven triangle, which describes a roughly inverted equilateral triangular region on the chest having a base extending between the left and right shoulder joints and an apex approximately located at the base of the ribcage, below the sternum. The model contemplates the angle formed at the right shoulder having a first aspect of the ECG signal, the abdominal angle having a second such aspect and the left shoulder angle having a third aspect. The Bayley triaxial system and the Hexaxial system each divide the chest and abdominal area into a larger number of sections or regions, each of which is assigned a single aspect or mixed aspect of the ECG signal.
All of the prior art location identification systems require electrodes placed in at least two of the quadrants of the body. The surface area of each quadrant is defined herein, therefore, as an equivalence region on the body, the portions of the body near the boundaries of the quadrants are further eliminated from such equivalence regions, as it is commonly understood that the boundary can move slightly as the heart beats, the person moves, and that the boundaries can be different between different individuals due to minor difference in heart orientation within the body. The equivalence regions are thus defined as the quadrants illustrated in
Several prior art devices exist for measuring ECG based on the traditional model. For example, clinical or medical ECG devices use several electrodes placed on the chest, arms and legs to measure a number of different ECG signals from selected electrode pairs wherein in each pair, one electrode is located in one equivalence region and the other electrode is located in a different equivalence region. The different readings together allow a clinician to get a view of the function of the three dimensional electrical activity of the heart from a number of different angles. In many cases, the devices which provide the ability to detect and monitor the heart related parameters is stationary and is intended to monitor a stationary patient.
Such devices, while highly accurate, are very expensive and cumbersome and thus do not lend themselves well to ambulatory or long term uses such as in a free living environment. Holter monitors are devices that may be used for continuous, ambulatory ECG measurement, typically over a 24-48 hour time period. These Holter devices collect raw electrical data according to a preset schedule, or frequency, typically 128 hz or 256 hz. These devices must therefore contain a significant amount of memory and/or recording media in order to collect this data. The physical bulk and inconvenient accessories of this device restricts its continuous use to a relatively short time frame. Each device comprises at least two electrodes for clinical or monitoring data detection and typically a third electrode for ground. The leads are designed to be attached to the chest across the heart, or at least across the conventionally understood sagittal plane 2, and a monitoring device connected to the electrodes is carried or worn by the patient, which is typically a heavy rectangular box clipped to the patient's waist or placed in a shoulder bag. The sensors utilized in conjunction with the device are affixed according to a clinical procedure, wherein the skin under the electrode or sensor is shaved and/or sanded and cleaned with skin preparation liquids such as alcohol prior to application to improve signal quality. Consequently, the sensors are not easily interchanged and may limit physical or hygienic activity. Holter monitors are relatively expensive and for the reasons listed above, are not comfortable for long term and/or active wear situations.
Loop monitors are configured and worn similarly, yet are designed to work for longer periods of time. These systems are designed to record shorter segments or loops of raw data or morphology when the wearer signifies, by pressing a time stamp button that they are doing an activity of interest or feeling a chest or heart related pain. The device will typically record 30 seconds before and 30 seconds after the time stamp. While some success with respect to longer term wearability and comfort is achieved, these loop monitor devices are still inconvenient for everyday use, and include lead wires from the device, snap on sensors affixed to the body by adhesives which require daily skin preparation and periodic re-alignment of the sensors to the original positions.
More recently, a few monitors have also been provided with some automated features to allow the device, without human intervention, to record certain loops when certain preset conditions or measurement thresholds are achieved by the detected heart related activity, such as an abnormal beat to beat interval or a spike in heart rate. Implantable loop recorders have also been developed, which provide similar functionality, with the attendant inconvenience and risks associated with an invasive implant.
Another diagnostic device is known as an event recorder, and this device is a hand held product, with two electrodes on the back, some desired distance apart with recording capabilities where a patient is instructed to place this device against the skin, over the heart or across the sides of the body in order to record a segment of data when the patient is feeling a heart related symptom. This device is not utilized for continuous monitoring, and has memory capability for only a limited number of event records. Once the media storage is filled, there is a facility on the device to communicate the data back to a clinic, clinician, service, or doctor for their analysis, usually by telephone.
While not designed for medial or clinical applications per se, a number of chest strap heart rate monitors have been developed that may be used to measure heart rate from ECG, with some recent devices being capable of recording each detected heart beat, recorded in conjunction with a time stamp in the data. Examples of such conventional monitors commercially available include Polar Electro Oy, located in Oulu, Finland and Acumen, Inc. located in Sterling, Va. These chest strap monitors are designed to be wrapped around the torso beneath the chest and include two electrodes positioned on either side of the heart's conventionally understood transverse plane 3 for measuring an ECG signal. The device is placed just below the pectorals, with conventional electrode positioning. The device is placed at this location because noise and motion signal artifacts from muscle activity is minimal and the signal amplitude is quite robust, consistent and discernable by a circuit or software application. Chest strap monitors of this type, while promoted for use in exercise situations, are not particularly comfortable to wear and are prone to lift off of the body during use, particularly when the wearer lies on his or her back.
Finally, a number of watch-type ECG based heart rate monitors are commercially available, such as the MIO watch sold by Physi-Cal Enterprises LP, located in Vancouver, British Columbia. Such watches include a first electrode attached to the back of the watch that, when worn, contacts one arm of the wearer, and one or more second electrodes provided on the front surface of the watch. To get an ECG signal, and thus a heart rate, a wearer must touch the second electrode(s) with a finger or fingers on the opposite hand, that is, the hand of the arm not wearing the watch. Thus, despite being worn on one arm, the watch measures ECG according to the conventional method, being across the heart, again on either side of the heart's conventionally understood sagittal plane 2, because the two electrodes are contacting both arms. Such watches, while comfortable to wear, only make measurements when touched in this particular manner and thus are not suitable for monitoring ECG and heart rate continuously over long periods of time or while conducting everyday activities such as eating, sleeping, exercising or even keyboarding at a computer.
Matsumara, U.S. Pat. No. 5,050,612, issued Sep. 24, 1991, discloses the use of a multi-electrode sensing watch device, identified as the HeartWatch, manufactured by Computer Instruments Corporation, Hampstead, N.Y., for certain types of heart parameter detection. While Matsumara discloses that the conventional use of the HeartWatch device is in conjunction with a chest strap, he also identifies an alternative use which relies solely on the multisensor watch device itself. The device has two electrodes at different distances along the arm from the heart, and the detected waveform from one electrode is subtracted from the other to obtain a resultant signal. Matsumura identifies this signal as not resembling an ECG, but states that it is useful for detecting ST segment depression. No teaching or suggestion of the efficacy of this method for the identification of heart rate or other heart related parameters is made.
As described above, the traditional models of ECG measurement do not contemplate the action potential of the heart, and thus ECG, being detected and measured from two points within a single quadrant or within a single equivalence region. Moreover, the traditional model rejects the measurement of the action potential from two locations on the same limb. The prior art does contemplate some sensor placements which take advantage of the three dimensional nature of the human body and allow for measuring the heart's action potential between electrodes placed on the front and back of the body, or between spots high on the torso and low on the torso, but on the same side of the body. One skilled in the art would recognize that the prior art only utilized sensor placements that included two or more electrodes in multiple quadrants or equivalence regions.
Another significant shortcoming of ambulatory devices is electrical noise. Noise is detected from both ambient sources surrounding the body, movement and organ noise within the body, and most significantly, the movement of the body itself, including muscle artifacts, motion artifacts, skin stretching and motion between the electrode and the skin. A variety of patents and other references relate to the filtering of noise in many systems, including heart rate detection. In Zahorian, et al., U.S. Pat. No. 5,524,631, issued Jun. 11, 1996, a system is disclosed for detecting fetal heart rates. A significant noise problem exists in that environment, including the heart action of the mother, as well as the significant noise and distortion caused by the fetus' location within a liquid sac inside the mother's abdomen. Zahorian utilizes multiple parallel non linear filtering to eliminate such noise and distortion in order to reveal the fetus' heart rate. The system, like many of the prior art, is unconcerned with the wearability of the monitoring device or the ability to continuously monitor the subject over a long period of time.
None of the above systems identified above combine wearability and accuracy in a compact device. What is lacking in the art, therefore, is a device which provides the ability to measure ECG from two locations in a single equivalence region, such as within a single quadrant as shown in
A monitor device and associated methodology are disclosed which provide a self contained, relatively small and wearable package for the monitoring of heart related parameters, including ECG. The monitor device is primarily a simple, unobtrusive housing which is wearable in the sense that it is temporarily affixed to the user's body, but also wearable in the sense described in Stivoric, et al., U.S. Pat. No. 6,527,711, issued Mar. 4, 2003, the disclosure of which is incorporated by reference hereto. Stivoric teaches that the sizing, flexibility and location of items attached to the body significantly affect the ability of the wearer to recognize the item as part of the body, reducing the irritation factor associated with wearing such an item for extended periods of time. Furthermore, the use of the appropriate shapes, materials and locations reduces the interference of the item with normal body movement and activity. Each of these factors increases the wearability of the item and therefore increases the compliance of the wearer with the need for long term and continuous wear.
More specifically, the monitor device may be of a type described in Teller, et al., U.S. Pat. No. 6,605,038, issued Aug. 12, 2003, the specification of which is incorporated herein by reference. The primary focus of the monitor device itself is to provide the functionality described below in a housing or other package which is comfortable for long term wear, remains in place during normal daily activity so as to continuously provide a quality signal or data record and also reduces the noise or other interference to that signal or record created by the device itself. One focus of the device is to provide a self-contained housing which incorporates all or at least the majority of the operating hardware. The monitor device, in addition to the Teller device, may further include, as an accessory or rigid housing substitute, a large sized adhesive strip, similar to that used for cuts and abrasions which contains the sensor package within the current location of the absorbent material. Reduction of weight and bulk is very important to increasing the ability for the device to remain affixed in both the right location and with proper contact to the body, especially under rigorous conditions, such as exercise. The device is easy to put on and take off without need for extensive or clinical skin preparation, if any. The device is provided with an appropriate type and strength of adhesive required to keep the weight of the device from disconnecting any snaps or other connections, or pulling the electrode off of the skin. One primary advantage of the device is the elimination of long lead wires which, in addition to being unsightly and inconvenient, act as large antennas for creating noise input to the system. Reduction in the amount of snap connections also reduces these noises, which are common for Holter and loop devices. While not necessarily possible with the current state of processor and sensor size, it is clearly contemplated that the instant system, given the appropriate miniaturization of hardware, could be as simple as sliding on a watch or pair of glasses, utilizing the same basic methodology and equipment identified herein.
Specifically, a monitoring device is disclosed which includes at least one or more types or categories of sensors adapted to be worn on an individual's body. The sensor or sensors, which may include multiple electrodes or other subordinate sensing devices of equivalent type, may be drawn from the categories of contextual and physiological sensors. The physiological sensors may be selected from the group consisting of: respiration sensors, temperature sensors, heat flux sensors, body conductance sensors, body resistance sensors, body potential sensors, brain activity sensors, blood pressure sensors, body impedance sensors, body motion sensors, oxygen consumption sensors, body chemistry sensors, blood chemistry sensors, interstitial fluid sensors, body position sensors, body pressure sensors, light absorption sensors, body sound sensors, piezoelectric sensors, electrochemical sensors, strain gauges, and optical sensors. Sensors are incorporated to generate data indicative of detected parameters of the individual. There may be one or more such parameters of the individual, with at least one such parameter being a physiological parameter. The apparatus also includes a processor that receives at least a portion of the data indicative of at least one physiological parameter. Preferably, the device is specifically directed to the detection of a single heart related parameter, heart beats. It is to be specifically understood that additional parameters may be detected with or without additional sensors. The processor may be adapted to generate derived data from at least a portion of the data indicative of such detected parameters, wherein the derived data comprises an additional parameter of the individual. The additional parameter is an individual status parameter that cannot be directly detected by any of the sensors.
The sensors may be physiological sensors, or may be at least one physiological sensor and one or more optional contextual sensors. The monitoring device may further include a housing adapted to be worn on the individual's body, wherein the housing supports the sensors or wherein at least one of the sensors is separately located from the housing. The apparatus may further include a flexible body supporting the housing having first and second members that are adapted to wrap around a portion of the individual's body. The flexible body may support one or more of the sensors. The apparatus may further include wrapping means coupled to the housing for maintaining contact between the housing and the individual's body, and the wrapping means may support one or more of the sensors.
The monitoring device may include, or, optionally, be utilized in conjunction with an external a central monitoring unit remote from the at least two sensors that includes a data storage device. The data storage device receives the derived data from the processor and retrievably stores the derived data therein. The apparatus also includes means for transmitting information based on the derived data from the central monitoring unit to a recipient, which recipient may include the individual or a third party authorized by the individual. The processor may be supported by a housing adapted to be worn on the individual's body, or alternatively may be part of the central monitoring unit.
As a further alternative embodiment, rather than the processor provided in the monitoring device being programmed and/or otherwise adapted to generate the derived or other calculated data, a separate computing device, such as a personal computer, could be so programmed. In this embodiment, the monitoring device collects and/or generates the data indicative of various physiological and/or contextual parameters of the user, which is stored in the memory provided. This data is then periodically uploaded to a computing device which in turn generates derived data and/or other calculated data. Alternatively, the processor of the monitoring device could be programmed to generate the derived data with the separate computer being programmed and/or otherwise adapted to include the utilities and algorithms necessary to create further or secondary derivations based on the physiological and/or contextual data, the first level data derived therefrom, data manually input by the user and/or data input as a result of device-to-device interaction uploaded from the monitoring device or a cooperative third device. The computing device in these alternative embodiments may be connected to an electronic network, such as the Internet, to enable it to communicate with a central monitoring unit or the like.
The apparatus may be further adapted to obtain or detect life activities data of the individual, wherein the information transmitted from the central monitoring unit is also based on the life activities data. The central monitoring unit may also be adapted to generate and provide feedback relating to the degree to which the individual has followed a suggested routine. The feedback may be generated from at least a portion of at least one of the data indicative of a physiological parameter, the derived data and the life activities data.
The central monitoring unit may also be adapted to generate and provide feedback to a recipient relating to management of an aspect of at least one of the individual's health and lifestyle. This feedback may be generated from at least one of the data indicative of a first parameter, the data indicative of a second parameter and the derived data. The feedback may include suggestions for modifying the individual's behavior.
The system is designed to collect data continuously, with no interaction of the wearer necessary, but such interaction is permitted for additional functionality such as particular time stamping capabilities, as necessary. The ability to continuously monitor the heart related parameters limits the need for a manual trigger at the time of an event or the detection of a threshold condition based upon the status of the derived data, as described above. While the system is designed to collect data continuously, in some embodiments the user may utilize the timestamp button to signal that certain heart rate parameters should be collected for the time period around the timestamp. An additional functionality of the device is context and activity detection. Through the use of both the physiological and contextual sensors provided in the device, the ability to learn, model, or ascertain what combinations of data parameters relate to certain activities can be achieved. The ability to detect and discern the type of activity in which the user is engaged relieves the user of the need to manually log these activities to correlate with the heart output data during subsequent review.
The functionality of the monitoring devices is predicated upon the detection of multiple inequipotential heart parameter signals within a single equivalence region of the body and more particularly, multiple detectable action potential signals on a single limb. The device and methods identify and monitor certain pairs of points on the body to obtain inequipotential signals with respect to the heart's action potential. The location of the sensors is therefore determined by their relationship to these detectable inequipotential action potential signals, which may be arranged about the planes illustrated in
It is specifically contemplated that the physical form and/or housing for the device is not limited to those embodiments illustrated herein. Additional embodiments which require more flexibility, or are intended to be disposable in nature may eliminate the housing entirely and include the electronic and other functionality in a more temporary or flexible container, such as a patch, which may have tentacle-like extensions or separately wired sensors attached thereto. Preferred locations for the device itself include, the deltoid and tricep upper arm locations identified specifically herein, the back of the base of the neck and adjacent medial shoulder area, the sides of the chest adjacent to the upper arms when at rest along the sides of the body and the femoral areas of the left and right lower front abdomen adjacent the pelvis.
Additionally, the device may be combined with other like devices in a cooperative array, which may be utilized to further process or analyze the signals derived therefrom. For example, in the case of a pregnant woman, a first such device may be positioned to detect the mother's heart related parameters in a location unlikely to detect the fetal heart related parameters and a second such device, especially in the form of an adhesive or other patch, might be located immediately adjacent the fetus on the mother's abdomen. The signals from the mother's device could be utilized to eliminate the noise of the mother's heart related parameters from the fetus' data stream.
Feedback from the system can take many forms, including the standard visual graphical methods, but a preferred embodiment would include audio feedback as well. This audio component may be in the form of a sound that resonates/conducts through the body, like a bone phone or other variant, to make this feeling more intimate and body like, even if the sound is manufactured digitally to represent the beat. A digital or analog stethoscope could be included in the system to assist in the production of an appropriate sound. Such a device on the abdomen could alternatively be made up of an array of Doppler or ECG electrodes to reduce the need to search for the most appropriate signal location. The device may also be adapted to work in conjunction with an implantable device or other consumed data detector.
Further features and advantages of the present invention will be apparent upon consideration of the following detailed description of the present invention, taken in conjunction with the following drawings.
The invention and the following detailed description of certain embodiments thereof may be understood by reference to the following figures:
Conventional thinking in the field of cardiology/ECG is that an ECG signal must be measured across the heart, meaning with electrodes placed in two different quadrants of the heart's conventionally defined sagittal and transverse planes. A device and methodology are disclosed herein which permits the measurement of an ECG signal from certain pairs of points located within regions or areas of the human body previously considered inappropriate for such measurement. The device and methodology disclosed herein focus on the identification of certain locations on the body within the previously defined equivalence regions utilized for electrode location. Many of these electrode locations are within a single quadrant, i.e., when the electrode locations are connected geometrically directly through tissue, the line described thereby does not cross into another quadrant.
In other words, certain points within one quadrant are correlated with the electropotential of the ECG signal conventionally associated with a different quadrant because the potential from the opposite side has been transported to that point internally through what appear to be low impedance non-homogeneous electropotential or electrical pathways through the body, which may be analogized as internal signal leads within the tissue. This methodology therefore focuses on two different aspects of the ECG signal, rather than more narrowly defining these aspects as emanating from certain quadrants of the body. Thus, contrary to the teachings of the prior art, an ECG signal may be detected and measured using pairs of electrodes placed within a single quadrant, but detecting a significant electrical potential difference between the two points. In other words, the two points are inequipotential with respect to one another. In most instances, it is more helpful to envision the electrode locations being located within independent regions of skin surface, separated by a boundary which may be planar or irregular.
In the preferred embodiment of the present invention, pairs of locations on or near the left arm have been identified for placement of electrodes to detect the different aspects of the ECG signal. It is to be noted that similar sites within equivalence regions are found at a myriad of locations on the human body, including the right and left arms, the axillary area under the arms, the anterior femoral area adjacent the pelvis, the back of the base of the neck and the base of the spine. More specifically, certain locations on the left arm carry an aspect of the ECG signal and certain locations on or near the left arm carry a different aspect of the ECG signal. It is also to be specifically noted that anatomical names, especially names of muscles or muscle groups, are used to identify or reference locations on the body, though placement of the electrodes need only be applied to the skin surface directly adjacent these locational references and are not intended to be invasive. Referring now to
Thus, by placing one electrode on the wrist 5, triceps muscle 10 or the brachialis muscle 15 and a second electrode on the deltoid muscle 20, the teres major muscle 25 or the latissimus dorsi muscle 30, it is possible to detect the action potential of the heart and thus an ECG signal. The electrodes are preferably located near the central point of the deltoid and tricep muscles, are spaced approximately 130 mm and more particularly 70-80 mm apart and tilted at approximately 30-45 degrees toward the posterior of the arm from the medial line, with 30 degrees being most preferred. While certain specific preferred locations on or near the left arm have been described herein as being related to the electropotential of the second aspect of the ECG signal, it should be appreciated those locations are merely exemplary and that other locations on or near the left arm that are related to the electropotential of the second aspect of the ECG signal may also be identified by making potential measurements. It is further to be specifically noted that the entire lower arm section 5′ is identified as providing the same signal as wrist 5. Referring now to
In another embodiment, pairs of locations on or near the right arm for placing electrodes to detect an ECG signal are identified. Referring to
Referring now to
Similarly, it should be understood that the present invention is not limited to placement of pairs of electrodes on the left arm or the right arm for measurement of ECG from within quadrants I or III, as such locations are merely intended to be exemplary. Instead, it is possible to locate other locations within a single quadrant. Such locations may include, without limitation, pairs of locations on the neck, chest side and pelvic regions, as previously described, that are inequipotential with respect to one another Thus, the present invention should not be viewed as being limited to any particular location, but instead has application to any two inequipotential locations within a single quadrant.
One of the primary challenges in the detection of these signals is the relatively small amplitudes or differences between the two locations. Additionally, these low amplitude signals are more significantly masked and/or distorted by the electrical noise produced by a moving body, as well as the noise produced by the device itself. Noise, in this context, refers to both contact noise created by such movement and interaction of the body and device, as well as electronic noise detected as part of the signal reaching the sensors. An important consideration for eliminating noise is increasing the differentiation between the desired signal and the noise. One method involves increasing signal strength by extending one sensor or sensor array beyond the arm, to the chest or just past the shoulder joint. Consideration must be given with sensor placement to two competing desirable outcomes: increased signal strength/differentiation and compactness of the sensor array or footprint. The compactness is, of course, closely related to the ultimate size of the device which houses or supports the sensors. Alternative embodiments, as described more particularly herein, include arrangements of sensors which strive to maintain a compact housing for the device while increasing distance between the sensors by incorporating a fly-lead going to a sensor location point located some short distance from the device itself, such as on the left shoulder, which is still within quadrant I, or even to the other arm. The system further includes an electronic amplification circuit to address the low amplitude signal.
Referring to
Electrodes 105A and 105B are held against the skin to sense the relatively small voltages, in this case on the order of 20 μV, indicative of heart muscle activity. Suitable electrodes include Red Dot™ adhesive electrodes sold by 3M, which are disposable, one-time use electrodes, or known reusable electrodes made of, for example, stainless steel, conductive carbonized rubber, or some other conductive substrate, such as certain products from Advanced Bioelectric in Canada. It should be noted that unlike the Advanced Bioelectric development, most current reusable electrodes typically have higher coupling impedances that can impact the performance of circuit 100. Thus, to counteract this problem, a gel or lotion, such as Buh-Bump, manufactured by Get Rhythm, Inc. of Jersey City, N.J., may be used in conjunction with electrodes 105A and 105B when placed in contact with the skin to lower the skin's contact impedance. In addition, the electrodes 105 may be provided with a plurality of microneedles for, among other things, enhancing electrical contact with the skin and providing real time access to interstitial fluid in and below the epidermis. Microneedles enhance electrical contact by penetrating the stratum corneum of the skin to reach the epidermis. It is beneficial to make the ECG signal measurements at a position located below the epidermis because, as noted above, the voltages are small, on the order of 20 μV, and the passage of the signal through the epidermis often introduces noise artifacts. Use of microneedles thus provides a better signal to noise ratio for the measured signal and minimizes skin preparation. Such microneedles are well known in the art and may be made of a metal, silicon or plastic material. Prior art microneedles are described in, for example, in U.S. Pat. No. 6,312,612 owned by the Procter and Gamble Company. Based on the particular application, the number, density, length, width at the point or base, distribution and spacing of the microneedles will vary. The microneedles could also be plated for electrical conductivity, hypoallergenic qualities, and even coated biochemically to also probe/sense other physiological electro-chemical signal or parameters while still enhancing the electrical potential for ECG measurement. The microneedles may also be adapted to simultaneously sample the interstitial fluid through channels that communicate with micro level capillary tubes for transferring fluid in the epidermis for sensing electrically, chemically, or electro chemically. Microneedles further enhance the ability of the electrodes to remain properly positioned on the skin during movement of the user. The use of microneedles, however, may limit the ability of the sensors to be mounted on a larger device or housing, as the weight of the larger device may cause the microneedles to shear during movement. In such instances, the microneedle-enhanced sensor may be separately affixed to the body as shown in several embodiments herein. Use of adhesives to supplement the use of microneedles, or alone on a basic sensor is also contemplated. As will be discussed further herein, the use of materials of different flexibilities or incorporating a elastomeric or spring-like responsiveness or memory may further improve sensor contact and locational stability.
In certain circumstances, it is important for a clinician or other observer of the user to determine whether the device has remained in place during the entire time of use, for the purposes of compliance with a protocol or other directive. The use of certain adhesives, or adhesives coupled with plastic or cloth in the nature of an adhesive bandage may be utilized to affix the device to the skin and which would be destroyed or otherwise indicate that removal of the device had occurred or been attempted.
For a wearer to accurately or most affectively place the system on their arm, it may be at least necessary to check that the device is situated in a proper orientation and location, even if the desired location of the electrodes includes an area with significant tolerance with respect to position. In one particular embodiment of the present invention, a device having an array of electrodes 105, such as armband monitoring device 300 described above, is placed in an initial position on the body of the wearer, with each of the electrodes 105 is in an initial body contact position. The device then makes a heart rate or other heart related parameter measurement as described above, and compares the measured signal to a what would be an expected signal measurement for a person having the physical characteristics of the wearer, which had been previously input into the system as more fully described herein, such as height, age, weight and sex. If the measured signal is meaningfully more degraded, as determined by signal to noise ratio or ratio of beat height to noise height, than the expected signal, which would be a preset threshold value, the device gives a signal, such as a haptic, acoustic, visual or other signal, to the wearer to try a new placement position for the device, and thus a new contact position for the electrodes 105. A second measurement is then made at the new position, and the measured signal is compared to the expected signal. If the measured signal is meaningfully more degraded than the expected signal, the new position signal is given once again to the wearer. This process is repeated until the measured signal is determined by the device to be acceptable. When the measured signal is determined to be acceptable, the device generates a second success signal that instructs the wearer to leave the device in the current placement location. The device may initiate this operation automatically or upon manual request.
Circuit 100 also includes bias/coupling network 110, shown as two boxes in
Two approaches to providing bias current for the amplifier inputs are shown in
Although not specifically described, the bias/coupling network can be dynamic, in that adjustments can be made based upon the signals being produced when the device is first engaged, or under changing context conditions. This dynamic capability would also accommodate individual differences in amplitude for different placements of similar devices because of user size or other physical characteristics. Experimentation has shown some degree of variation on signal strength based upon distance. Furthermore, changes in signal are expected based on the amount of motion the device is doing relative to the arm, the flexing of the electrodes and their contact with the skin, contractions and relaxations of the muscles below or around the skin contact points and the movement of the body.
Preferably, bias/coupling network 110 employs capacitive input coupling to remove any galvanic potential (DC voltage) across electrodes 105A and 105B when placed on the body that would force the output of first stage amplifier 115 outside of its useful operating range. In addition, the non-zero input bias current of first stage amplifier 115 requires a current source/sink to prevent the inputs from floating to the power supply rails. In one embodiment, bias/coupling network 110 may take the form shown in
Referring to
Referring again to
A suitable example of first stage amplifier 115 is shown in
Key parameters in selecting an amplifier for first stage amplifier 115 are input bias current, input offset current, and input offset voltage. Input bias current multiplied by the input impedance of the bias/coupling network gives the common-mode input offset voltage of the positive and negative inputs to first stage amplifier 115. Care must be taken to keep the inputs of first stage amplifier 115 far enough from the power supply rails to prevent clipping the desired output signal. As with the bias/coupling network, an alternative design might include a circuit which was able to dynamically limit the input voltage based upon the type of activity, such as power on, initial attachment to the arm, or certain high-motion activities so that the input voltage under normal conditions would be optimum. As one skilled in the art would appreciate, some clipping can be acceptable. Algorithms for detecting heart rate or other heart parameters can work in the presence of some amount of clipping, assuming that the signal to noise ratio remains relatively high.
The input offset current parameter multiplied by the bias impedance gives the differential input voltage that is applied to first stage amplifier 115. This differential voltage is in addition to the input offset voltage parameter that is inherent in the amplifier, and the total input offset is simply the sum of the two. The total differential input voltage multiplied by the gain determines the output offset. Again, care must be taken to keep the output signal far enough from the power supply rails to prevent saturation of the amplifier output. As an example, a bipolar amplifier such as the model AD627 described above has an input bias current of 10 nA, an input offset current maximum of 1 nA, and an input offset voltage of 150 μV (all values are worst case maximums at 25° C.). In order to keep the common-mode input offset to less than 0.5 V, the bias impedance must be no more than 0.5 V/10 nA=50 MΩ. However, the input offset current dictates that in order to maintain a maximum 0.5 V output offset voltage, one must provide an input impedance of no more than 0.5 V/gain/1 nA. For a gain of 100, this resolves to 5 MΩ. For a gain of 500, this resolves to 1 MΩ. Another candidate amplifier for use as first stage amplifier 115 is the Texas Instruments Model INA321 programmable gain amplifier, which has FET inputs. This amplifier has an input bias current of 10 pA and an input offset current of 10 pA (max). In order to keep the common-mode input offset to less than 0.5 V, one must provide an impedance of no more than 0.5 V/10 pA=50 GΩ. However, the input offset current dictates that in order to maintain a maximum 0.5 V output offset, one must provide an input impedance of no more than 0.5 V/gain/10 pA. For a gain of 100, this resolves to 500 MΩ. For a gain of 1,000, this resolves to 50 MΩ.
As an alternative, as will be appreciated by those of skill in the art, first stage amplifier 115 may be implemented in a network of low cost discrete op-amps. Such an implementation will likely reduce the cost and power consumption associated with first stage amplifier 115. As will also be appreciated by those of skill in the art, the same analysis of amplifier input bias current, output saturation, and input bias/coupling applies to such an alternative implementation.
Referring again to
In one embodiment, filter 150 includes switched capacitor low-pass and high-pass filters with adjustable cutoff frequencies to allow for experimentation. Such a filter 150 may be constructed using the model LTC1164—6 low-pass filter chip sold by Linear Technology Corporation followed by a model LTC1164 high-pass filter chip also sold by Linear Technology Corporation, which chips provide an eighth order elliptical filter with very sharp cutoff characteristics. Experimentation with this implementation has shown that a low-pass cutoff frequency of 30 Hz and a high-pass cutoff frequency of between 0.1 Hz and 3 Hz worked well. Although allowing for flexibility, this implementation is relatively expensive and was found to consume a significant amount of power.
An alternative implementation for filter 150 is shown in
Referring again to
Analog to digital converter 160 converts the analog waveform output by second stage amplifier 155 into a digital representation that can then be processed by one or more algorithms, as described more fully herein, to determine heart related parameters, such as heart rate, therefrom. Analog to digital converter 160 may be implemented using a 12 bit analog to digital converter with a 3 V reference at 32-256 samples per second. Such a device is integrated into the Texas Instruments MSP430F135 processor. Analog to digital converter 160 is connected to central processing unit 165, which reads the converted digital signal and performs one of the following functions: (i) it stores the raw digital signal to memory, such as flash or SRAM, for subsequent analysis; (ii) it stores a number of raw digital signals to memory and subsequently transmits them, wired or wirelessly, to a remote computer for analysis as described herein and/or display, such as display in real time; or (iii) it processes the raw digital signals using algorithms described herein provided on central processing unit 165 to determine heart related parameters, such as the timing and various sizes of heart beats, heart rate, and/or beat-to-beat variability. With respect to this last function, central processing unit 165 may, once heart beats and/or heart rate has been determined, perform a variety of tasks such as blink an LED for each beat or store heart rate information to memory. Optionally, central processing unit may provide operational control or, at a minimum, selection of an audio player device 166. As will be apparent to those skilled in the art, audio player 166 is of the type which either stores and plays or plays separately stored audio media. The device may control the output of audio player 166, as described in more detail below, or may merely furnish a user interface to permit control of audio player 166 by the wearer.
These functions can also be performed independently in sequence. For example, the data can be stored in real time in a data storage medium while being simultaneously analyzed and output. Subsequent processes can allow the system to retrieve earlier stored data and attempt to retrieve different information utilizing alternative algorithmic techniques or filters. Additionally, data from different points in the filtration process, described above, can be simultaneously stored and compared or individually analyzed to detect signal information which is lost at certain points in the process.
Referring to
It is to be specifically noted that the circuitry may be implemented in a minimal cost and component embodiment, which may be most applicable to a disposable application of the device. In this embodiment, the apparatus is not provided with a processor, only electrically separated electrodes for picking up a voltage difference, a gating mechanism for differentially passing current associated with voltage spikes, such as QRS signals and a mechanism for displaying characteristics of the passed through current. This apparatus may be powered by motion, battery, or solar power. Another option is to power the apparatus directly from the voltage potentials being measured. The display mechanism may be chemical, LCD or other low power consumption device. The voltage spikes charge up a capacitor with a very slow trickle release; a simple LED display shows off the charge in the capacitor. In another embodiment, a simple analog display is powered by the battery. The simple apparatus utilizes digital processing but no explicit processor; instead a simple collection of gates, threshold circuitry and accumulator circuitry, as would be apparent to one skilled in the art, based upon the descriptions above, controls the necessary preprogrammed logic.
The implementation shown in FIGS. 7 and 8A-F, which utilize an array of electrodes 105, is particularly useful and advantageous due to the fact that the signals detected by electrodes 105 can at times be saturated by muscle activity of the body, such as muscle activity in the arm in an embodiment where electrodes 105 are placed on locations of the arm. The heart beat related portion of the signals detected by electrodes 105 are coherent, meaning highly correlated, while the muscle activity noise portions of the signals tend to be incoherent, meaning not correlated. Thus, because of this coherent/incoherent nature of the different portions of signals, when the signals generated by electrodes 105 are summed, subtracted, averaged, multiplied or the like, by summation circuit 170, the heart beat related components will add to one another thereby producing better heart beat spikes having a higher signal to noise ratio, while the muscle noise related components will tend to wash or cancel one another out because the “hills” and “valleys” in those signals tend to be off phase from one another. The result is a stronger heart beat related signal with less muscle related noise.
There are multiple sources of noise that can affect the amplified signal that is input into analog to digital converter 160 shown in
The system is specifically designed to minimize the processing time delays and interruptions created by noise being processed and subtracted or filtered from the primary signal. As noise is processed and consuming processor resources, data must be stored and processed at a later time. It is important to return as quickly as possible to contemporaneous monitoring so as to avoid the build up of a backlog of data. The system utilizes a plurality of measurement techniques, such as described above to quickly identify and extract the primary signal and rapidly return to real time monitoring. Most particularly, the circuitry is designed to minimize DC wander within three beats of the heart.
In addition, another source of noise that may affect the signal input into analog to digital converter 160 is muscle noise caused by the electrical activity of muscles. Electromyography, or EMG, is a measurement of the electrical activity within muscle fibers, which is generally measured actively, but could also be measured passively, according to the method of subtraction or filtering of the most distorted signal described above, because it is affected most by muscle artifact and/or has very little if not any signal relating to the heart related electrical activity. While a subject is in motion, electrodes 105 for measuring ECG may also simultaneously pick up and measure EMG signals. Such contemporaneously measured EMG signals are noise to the ECG signal. Thus, according to an aspect of the present invention, ECG signal measurement can be improved by using separate electrodes to specifically measure an EMG signal, preferably from body locations that have a minimal or difficult to detect ECG signal. This separately measured EMG signal may then be used to reduce or eliminate EMG noise present in the separately and contemporaneously measured ECG signal using various signal processing techniques. In many cases, the EMG signal's amplitude may so overwhelm that ECG signal that either filtering or utilizing the above-described method may not result in a usable ECG signal. In these events, the use of a non-electrode sensor could be utilized in conjunction with electrodes in order to detect the relatively quiet ECG signal. This sensor may even replace the beat detection if it detected ECG peaks when the primary electrical signal clips, gets oversaturated or overwhelmed by the EMG signal. An example sensor is a micro-Doppler system, either as a single pick-up or an array, designed to pick up the mechanical rushing of blood or the like, past the Doppler signal, creating a pulse wave in which the peak could be recognized and timed as a beat. This embodiment could be tuned to a specific location or utilize an array of different sensors tuned to different depths in order to optimize and locate the best signal for each user. This array could also be utilized, through monitoring of different signals and signal strength, to locate the device at the best position on the arm through well known audible or visual feedback mechanisms. The device could also be tuned to certain individual characteristics detected over an introductory period of evaluation or tuned dynamically over a period of time. Under certain high noise circumstances, the mechanical signal might be substituted for the electrical ECG signal as part of the calculations. In order to make the mechanical and electrical wave align, timing and phase shift differences would have to be calculated and factored into the peak or beat recognition algorithm. This system could be also utilized for detection and measurement of pulse transit time, or PTT, of the wearer, as described more fully herein, allowing relative and/or absolute measurement of blood pressure could be derived or calculated.
Pulse transit time, or PTT, is the time that it takes a pulse pressure waveform created by a heart beat to propagate through a given length of the arterial system. The pulse pressure waveform results from the ejection of blood from the left ventricle of the heart and moves through the arterial system with a velocity that is greater than the forward movement of the blood itself, with the waveform traveling along the arteries ahead of the blood. PTT can be determined by measuring the time delay between the peak of a heart beat, detected using the R-wave of an ECG signal and the arrival of the corresponding pressure wave at a location on the body such as the finger, arm, or toe, measured by a device such as a pulse oximeter or other type of pressure detector. As blood pressure increases, more pressure is exerted by the arterial walls and the velocity of the pulse pressure waveform increases. The velocity of the pulse pressure waveform depends on the tension of the arterial walls; The more rigid or contracted the arterial wall, the faster the wave velocity. As a result, for a fixed arterial vessel distance, as PTT increases and pulse pressure waveform velocity decreases, blood pressure decreases, and as PTT decreases and pulse pressure waveform velocity increases, blood pressure increases. Thus, PTT can be measured and used to indicate sudden changes in real-time blood pressure.
In one embodiment, the same armband device includes the ability to detect the ECG signal and in conjunction with a micro Doppler array against the body, together create the PTT measurement. An aspect of the present invention relates to the measurement and monitoring of PTT. Specifically, the time of a heart beat peak can be determined using an ECG signal using electrodes 105 as described herein. The time of the arrival of the corresponding pressure wave at a given location on the body can be measured using any one of a number of pressure sensors. Such pressure sensors may include, but are not limited to, pulse oximeters, Doppler arrays, single piezoelectric sensors, acoustic piezoelectric sensors, fiber optic acoustic sensors, blood volume pressure or BVP sensors, optical plethysmographic sensors, micropower impulse radar detectors, and seismophones. According to a preferred embodiment of the present invention, PTT is measured and monitored to indicate changes in blood pressure using armband body monitoring device 300 that is provided with one or more of the pressure sensors described above. Thus, in this embodiment, PTT is measured in a single device that obtains an ECG signal from the upper arm and that measures the arrival of the pulse pressure waveform at a location on the upper arm. Alternatively, the pressure sensor may be located separately from armband body monitoring device 300 at a different location, such as the finger or wrist, with the information relating to the arrival time being transmitted to armband body monitoring device 300 for calculation. This calculation may also be made at the finger product, or other third product, or shared between any combination of the above. Communication between each device can be provided in a wired or wireless embodiment, or transmitted through the skin of the wearer, as is well known to those skilled in the art.
An alternative embodiment includes the incorporation of third party devices, not necessary worn on the body, collect additional data to be utilized in conjunction with heart parameter data or in support thereof. Examples include portable blood analyzers, glucose monitors, weight scales, blood pressure cuffs, pulse oximeters, CPAP machines, portable oxygen machines, home thermostats, treadmills, cell phones and GPS locators. The system could collect from, or in the case of a treadmill or CPAP, control these devices, and collect data to be integrated into the streams for real time or future derivations of new parameters. An example of this is a pulse oximeter on the user's finger could help measure PTT and therefore serve a surrogate reading for blood pressure. Additionally, a user could utilize one of these other devices to establish baseline readings in order to calibrate the device.
In one specific embodiment, electrodes 105 may be placed on the deltoid muscle and the triceps muscle of the left arm in order to measure an ECG signal, which will likely contain muscle related noise, and separate electrodes 105 may be placed one each on the triceps muscle or one on the triceps muscle and one on the brachialis muscle for collecting an EMG signal having little or no ECG component, according to at least one of the several embodiments of the device more fully described below. This EMG signal may then be used to process and refine the measured ECG signal to remove the EMG noise as described herein. An example of such a configuration is armband body monitoring device 300 described below in connection with the specific alternative embodiments of the device, and more specifically
Although muscle noise can be reduced using separate EMG sensors as just described, it has been found that this noise, to a degree, often ends up remaining in the signal input into analog to digital converter 160 despite efforts to eliminate or reduce such noise. The amplitude of actual heart beat spikes, which comprise the QRS wave portion of the ECG signal, in the collected signal may vary throughout the signal, and the remaining muscle noise may obscure a heart beat spike in the signal or may itself look like one or more heart beat spikes. Thus, an aspect of the present invention relates to various processes and techniques, implemented in software, for identifying and reducing noise that is present in the digital signal output by analog to digital converter 160 and identifying heart beats and heart beat patterns from that signal. In addition, there may be portions of the signal that, despite processing efforts, contain too much noise and therefore no discernable heart related signal. A further aspect of the present invention relates to process and techniques for dealing with such portions and interpolating the data necessary to provide continuous and accurate output.
According to a one embodiment of the present invention, the signal that is output by analog to digital converter 160 may first undergo one or more noise reduction steps using software residing on either CPU 165 or on a separate computer to which the signal has been sent. For example, in one possible noise reduction implementation, the signal is first processed to identify each peak in the signal, meaning an increasing amplitude portion followed by a maximum amplitude portion followed by a decreasing amplitude portion. An example of such a peak is shown in
Another method for eliminating noise is that of filtering the signal in software residing either on either CPU 165 or on a separate computer to which the signal has been sent. In the preferred embodiment, this filtering consists of a non-linear filter designed to accentuate differences between noise and heartbeats.
While these noise reduction steps are likely to remove a significant amount of noise from the signal received from analog to digital converter 160, it is likely that, notwithstanding this processing, there will still be noise remaining in the signal. This noise makes the task of identifying actual heart beat spikes from the signal for purposes of further processing, such as calculating a heart rate or other heart related parameters, difficult. Thus, a further aspect of the present invention relates to various processes and techniques, again implemented in software residing on either CPU 165 or a separate computer, for identifying heart beat spikes from the signal notwithstanding any remaining noise. As will be appreciated, these processes and techniques, while preferably being performed after one or more of the noise reduction steps described above, may also be performed with any prior noise reduction steps having been performed.
As is well-known in the prior art, the Pan-Tompkins method uses a set of signal processing frequency filters to first pass only the signal that is likely to be generated by heart beats, then proceeds to differentiate, square and perform a moving window integration on the passed signal. The Pan-Tompkins method is described in Pan, J. & Tompkins, W. J., “A Real-time QRS Detection Algorithm,” IEEE Transactions on Biomedical Engineering, 32, 230-236 (1985), the disclosure of which is incorporated herein by reference.
According to this aspect of the invention, areas in the signal output by analog to digital converter 160 (with or without noise reduction as described above) having excessive noise, i.e., too much noise to practically detect acceptable heart beat spikes from the signal, are first identified and marked to be ignored in the processing. This may be done by, for example, identifying areas in the signal having more than a predetermined number of rail hits or areas in the signal within a predetermined time window, e.g., ¼ of a second, of two or more rail hits. Next, the remaining areas, i.e., those not eliminated due to too much noise being present, referred to herein as the non-noise signal, are processed to identify acceptable heart beat spikes for use in calculating various heart parameters such as heart rate.
In one embodiment of the present invention, acceptable heart beat spikes are identified in the non-noise signal by first identifying and then calculating the height and width of each peak in the non-noise signal as described above. Next, the width of each peak is compared to a predetermined acceptable range of widths, and if the width is determined to be within the acceptable range, the height of the peak is compared to an adaptive threshold height equal to 0.75 of the moving average of the height of the previous peaks. Preferably, the acceptable range of widths is 3 to 15 points when a 128 Hz analog to digital sampling rate is used, and represents a typical range of widths of a QRS portion of an ECG signal. Next, if the width of the current peak is within the acceptable range and if the height of the peak is greater than the adaptive threshold, then that peak is considered a candidate to be an acceptable peak for further processing. Peaks not meeting these requirements are ignored. Next, for candidate acceptable peaks within a predetermined timeframe of one another, preferably 3/16 of a second of one another, the heights of the peaks are compared to one another and the lower peaks in that time frame are ignored. If there is only one candidate acceptable peak within the timeframe, then that peak is considered a candidate acceptable peak. At this point, a number of candidate acceptable peaks will have been identified. Next, for each identified candidate acceptable peak, the area between that peak and the last, being that immediately previous in time, candidate acceptable peak is examined for any other signal peaks having a height that is greater than 0.75 of the height of the current candidate acceptable peak. If there are more than a predetermined number, preferably 2, such peaks identified, then the current candidate acceptable peak is invalidated and ignored for further processing. In addition, if there are any hits of the rail as described above between the last candidate acceptable peak and the current candidate acceptable peak, then the current candidate acceptable peak is invalidated and ignored for further processing. When these steps are completed, a number of acceptable peaks will have been identified in the signal, each one being deemed an acceptable heart beat spike that may be used to calculate heart related parameters therefrom, including, but not limited to, heart rate.
According to an alternate embodiment for identifying acceptable heart beat spikes, each up-down-up sequence, a possible QRST sequence, in the non-noise signal is first identified. As used herein, an up-down-up sequence refers to a sequence on the non-noise signal having an increasing amplitude portion followed by a maximum amplitude portion followed by a decreasing amplitude portion followed by a minimum amplitude portion followed by an increasing amplitude portion. An example of such up-down-up sequence is shown in
Next, the height of each up-down-up sequence is compared to a predetermined threshold value, preferably an adaptive threshold such as some percentage, e.g., 75%, of the moving average of previous heights, and the width of each up-down-up sequence is compared to a predetermined threshold value range, preferably equal to 4 to 20 points when a 128 Hz analog to digital sampling rate is used, which represents a typical range of widths of a QRST sequence of an ECG signal. If the height is greater than the threshold and the width is within than the predetermined threshold value range, then that up-down-up sequence is considered to be a candidate acceptable QRST sequence. Next, for each identified candidate acceptable QRST sequence in the non-noise signal, a surrounding time period window having a predetermined length, preferably 3/16 of a second, is examined and the height of the current candidate acceptable QRST sequence in the time period window is compared to all other identified candidate acceptable QRST sequences in the time period window. The candidate acceptable QRST sequence having the largest height in the time period window, which may or may not be the current candidate acceptable QRST sequence, is validated, and the other candidate acceptable QRST sequences in the time period window, which may include the current candidate acceptable QRST sequence, are invalidated and ignored for further processing. Once this step has been completed, a number of acceptable QRST sequences will have been identified in the non-noise signal. Next, for each acceptable QRST sequence that has been identified, the distance, in terms of time, to the immediately previous in time acceptable QRST sequence and the immediately next in time QRST sequence are measured. Each distance is preferably measured from the R point of one sequence to R point of the other sequence. The R point in each acceptable QRST sequence corresponds to the point B shown in
Those sequences that are determined to be too noisy are ignored. Thus, upon completion of this step, a set of acceptable QRST sequences will have been identified, the QRS, which corresponds to points A, B and C in
According to an alternate embodiment for identifying acceptable heart beat spikes, each up-down-up sequence, a possible QRST sequence, in the filtered signal is first identified. The heights of the components of the sequence are then calculated. The allowed amplitude of the candidate QRST complexes are required to be at least double the estimated amplitude of signal noise. In addition, the width of the sequence must not exceed 200 milliseconds, an upper limit for believable QRST complexes. Next, if a candidate QRS complex is still viable, the plausibility of the location in time for the complex given the current heart rate estimate is checked. If the change in heart rate implied by the candidate beat is less than fifty percent then the sequence is identified to be a heart beat.
Additional methodologies are presented for the analysis and display of the heart rate data. In each of these methods, the signal is serially segmented into a set of overlapping time slices based on identified QRST sequences. Each time slice is preferably exactly centered on the R point of a sequence and contains a fixed window of time, e.g. 1.5 seconds, on either side of the R point of that sequence. Each time slice may contain more than one QRST sequence, but will contain at least one in the center of the time slice. While the analysis is performed mathematically, a graphical description will provide the clearest understanding to those skilled in the art. Next, for a given point in time, some number of time slices before and after a given time slice are merged together or overlaid on the same graph. In one particular embodiment, 10 time slices before and after a given point are overlaid on the same graph In terms of graphic display, which is how this data may be presented to the user in the form of output, the time slice segments are overlapped, whereby some number of QRST sequences, or time slice segments, are overlaid on the same graph. Each detected primary QRST sequence and the neighboring sequences within the time slice segment, preferably 1.5 seconds, are overlaid on top of the other beats in that window. For example, in
Another embodiment of the overlapping-beat-graph involves using a ADD-based approach to overlaps. In this version, as illustrated in
A method of establishing a database or other reference for the morphologies of the user's heart beat signal would necessarily include the ability to classify heart beat patterns and to identify certain morphologies. These patterns and morphologies could then be associated with certain activities or conditions. The first step, however, is to identify the morphologies and patterns, as follows.
For example, a set of N ECG wave forms may be selected. The average distance between beats is identified and a time period ½ of the interbeat period before and ½ of the interbeat period after to truncate each waveform. It is specifically noted that other clipping distances are possible and could be variable. As with the descriptions of beat matching above, a graphic description of the process is the most illuminating. N signal wave forms are detected in the clipping mode and are modeled, as with the ADD graphs above, with the signal features being measured by the intensity or brightness. The signal is assigned an intensity or numerical value. The surrounding area has no value. The equator line of each wave form is identified, being that horizontal line such that the areas above and below this line are equal. A meridian line is identified for each wave peak as that vertical line that subdivides the QRS spike into two pieces, split at the peak value of the signal. All N images are overlapped such that all equators are coincident and all meridians are coincident. All intensity or numerical values for each point in the N signals are normalized such that all values are between two known boundary values, such as 0 and 1000. The result is a representation that captures the average heart beat morphology for that person over that period of time including, within the non-coincident areas, signal segments where the wave forms tend to be most coincident, having the highest values and the least coincident, having the lowest values. In addition, each of the N images could be scaled prior to overlap, wherein the height of the R point of each wave forms a constant. Additionally, accuracy may be increased by selecting X segments of X wave forms in row and performing the above analysis with the sequence of X wave forms instead of just with one.
As will be appreciated by those of skill in the art, it is possible that the signal output by analog to digital converter 160 may have its polarity inverted as compared to what is expected from an ECG signal due to the placement of electrodes 150, in which case what would otherwise be peaks in the signal will appear as valleys in the signal. In such a case, the processing described above may be successfully performed on the signal by first inverting its polarity. In one embodiment of the present invention, the signal output by analog to digital converter 160 may be processed twice as described above, first without inverting its polarity and then again after its polarity has been inverted, with the best output being used for further processing as described herein. Additionally, the use of multiple sensors, such as an accelerometer or alternative pairs of electrodes, can be utilized to direct variable gain and dynamic signal thresholds or conditions during the signal processing in order to better adjust the types or nature of the processing to be applied. Additionally, a peak detector circuit may be employed such as that manufactured by Salutron, Fremont, Calif.
In addition, the system may detect known and recognizable contexts or signal patterns that will simply not present an acceptable signal that is discernable by the algorithms for beat and other body potential related feature detection. In these situations, the system simply recognizes this condition and records the data stream, such as when EMG or motion amplitude is at a peak level, the system detects this condition and discontinues attempting to process the signal until the next appropriate signal is received, according to certain preset or dynamically calculated conditions or thresholds. In some cases, the output of other sensors may be utilized to confirm the presence of a condition, such as excessive body motion, which would confirm that the system is operating properly, but lacking a coherent signal, as well as provide a basis for interpolation of the data from the missing segment of time. Under these conditions, a returned value from the system that no heart information could reliably collected is itself of value, relative to returning erroneous heart information.
Once acceptable heart beat spikes have been identified from the signal that is output by analog to digital converter 160 using one of the methods described herein, the acceptable heart beat spikes may be used to calculate heart rate using any of several methods. While merely counting the number of acceptable heart beat spikes in a particular time period, such as a minute, might seem like an acceptable way to calculate heart rate, it will be appreciated that such a method will actually underestimate heart rate because of the fact that a number of beats will likely have been invalidated as noise as described above. Thus, heart rate and other heart related parameters such as beat to beat variability and respiration rate must be calculated in a manner that accounts for invalidated beats. According to one embodiment, heart rate may be calculated from identified acceptable heart beat spikes by determining the distance, in time, between each group of two successive acceptable heart beat spikes identified in the signal and dividing sixty seconds by this time to get a local heart rate for each group of two successive acceptable heart beat spikes. Then, an average, median and/or peak of all of such local heart rates may be calculated in a given time period and used as the calculated heart rate value.
In the event that a period of time is encountered where no signal is available of a minimum level of quality for beat detection, a methodology must be developed by which the events of this time period are estimated. The system provides the ability to produce accurate statements about some heart parameters, including heart rate, for this missing time period. A probability is assigned to the heart beat frequency based upon the prior data which is reliable, by taking advantage of previously learned data and probabilities about how heart rates change through time. This is not limited to the time period immediately prior to the missing time segment, although this may be the best indicator of the missing section. The comparison can also be made to prior segments of time which have been stored and or categorized, or through matching to a database of information relating to heart parameters under certain conditions. The system can also take advantage of other sensors utilized in conjunction with the device in these computations of probability. For example the probability of missing heart beats on the heart beat channels can be utilized given that the variance of the accelerometer sensor is high. This enables very accurate assessments of different rate sequences and allows the calculation of a likely heart rate. This method is most successful when some minimum number of detected beats are present.
An additional method of estimating activity during missing time periods is to first identify candidate beats using one of the methods discussed above. Any detection technique that also produces a strength value can be used. In the preferred embodiment the detector will associate a probability that the located beat is in fact a heart beat. Binary true/false detectors can be used by using as strength value 1 for truth. Next, all pairs of potential beats are combined to give a set of inter-beat gaps. Each inter-beat gap defines a weighting function whose values are based on a combination of the size of the gap, the amount of time which has passed since the gap was detected, the strength of the identification and any meta-parameters needed by the family of weighting functions. In the preferred embodiment this weighting function is the inverse notch function. The inter-beat gap, in units of seconds, determines the location of the notch's peak. The height of the notch is driven by the strength of the identification, the length of time since the gap was identified, as age, and a hyper-parameter referred to as lifetime. The width of the notch is defined by the hyper-parameter width.
In the third step, the individual weighting functions are summed to obtain a total weighting function. Finally, the resulting function is programmatically analyzed to obtain an estimate of heart rate.
In the preferred embodiment, the estimate of the true inter-beat gap is taken to be the value at which the function reaches its first local maximum.
To minimize the processing load associated with the evaluation of the total weighting function, those individual weighting functions whose inter-beat gaps are either larger or smaller than is physiologically possible are eliminated. In addition, individual functions whose age has exceeded the value of the lifetime hyper parameter are also eliminated.
Another embodiment utilizes probabilistic filters on the allowed inter-beat gaps instead of a hard truncation as described above. These probabilistic filters take as input one or more signals in addition to the ECG signal and determine a probabilistic range for the allowable heart beat. One instantiation of this is to determine the context of the wearer from the non-ECG signals and then, for each context, to apply a particular Gaussian distribution with parameters determined by the context, the wearer's body parameters, as well as the ECG signal itself. Other probability distributions can easily be utilized as well for this biasing. This probability can then be multiplied by the probability of each inter-beat gap to produce a posterior distribution, from which the most likely heart beat can be easily determined.
Another aspect of the present invention is that during times when certain heart parameters are not computable due to noise, these parameters can also be estimated from the set of measured values nearby in time and the sequences of other measurements made on other sensors. One such embodiment of this method is a contextual predictor similar to that used for energy expenditure, but instead used to predict heart rate from accelerometer data, galvanic skin response data, skin temperature and cover temperature data, as well as steps taken and other derived physiological and contextual parameters. This method first identifies the wearer's activity, and then applies an appropriate derivation for that activity. In the preferred embodiment, all derivations for all activities are applied and combined according to the probability of that activity being performed.
An additional aspect of the invention is a method of adaptation over time for a particular user through the use of multiple noisy signals that provide feedback as to the quality of other derived signals. Another way of viewing this is as a method of calibration for a given user. First, a given derived parameter is calculated, representing some physiological state of the wearer. Second, a second derived parameter is calculated, representing the same physiological state. These two derived parameters are compared, and used to adjust one another, according to the confidences calculated for each of the derived metrics. The calculations are designed to accept a feedback signal to allow for training or tuning them. In one embodiment, this consists of merely utilizing gradient descent to tune the parameters based on the admittedly noisy feedback signal. In another embodiment, this involves updating a set of constants utilized in the computation based on a system of probabilistic inference.
According to one aspect of the present invention, an algorithm development process is used to create a wide range of algorithms for generating continuous information relating to a variety of variables from the data received from the plurality of physiological and/or contextual sensors on armband body monitoring device 300, as identified in Table I hereto, including the ECG signal generated using electrodes 105 that is used to calculate heart rate and other heart related parameters, many of which cannot be distinguished by visual recognition from graphical data output and diagnostics alone. These include heart rate variability, heart rate deviation, average heart rate, respiration rate, atrial fibrillation, arrhythmia, inter-beat intervals, inter-beat interval variability and the like. Additionally, continuous monitoring of this type, coupled with the ability to event- or time-stamp the data in real time, provides the ability to titrate the application of drugs or other therapies and observe the immediate and long term effects thereof. Moreover, the ability is presented, through pattern recognition and analysis of the data output, to predict certain conditions, such as cardiac arrhythmias, based upon prior events. Such variables may include, without limitation, energy expenditure, including resting, active and total values; daily caloric intake; sleep states, including in bed, sleep onset, sleep interruptions, wake, and out of bed; and activity states, including exercising, sitting, traveling in a motor vehicle, and lying down. The algorithms for generating values for such variables may be based on data from, for example, an axis or both axes of a 2-axis accelerometer, a heat flux sensor, a GSR sensor, a skin temperature sensor, a near-body ambient temperature sensor, and a heart rate sensor in the embodiments described herein. Additionally, through the pattern detection and prediction capabilities described above, the system may predict the onset of certain events such as syncope, arrhythmia and certain physiological mental health states by establishing a known condition set of parameters during one such episode of such an event and detecting similar pre-event parameters. An alarm or other feedback would be presented to the user upon the reoccurrence of that particular set of parameters matching the prior event.
The monitoring device is capable of generating data indicative of various additional physiological parameters of an individual which would be helpful as part of the predictive and parameter identification functionality described above. This includes, in addition to those parameters described elsewhere, respiration rate, skin temperature, core body temperature, heat flow off the body, galvanic skin response or GSR, EMG, EEG, EOG, blood pressure, body fat, hydration level, activity level, oxygen consumption, glucose or blood sugar level, body position, pressure on muscles or bones, and UV radiation exposure and absorption. In certain cases, the data indicative of the various physiological parameters is the signal or signals themselves generated by the one or more sensors and in certain other cases the data is calculated by the microprocessor based on the signal or signals generated by the one or more sensors. Methods for generating data indicative of various physiological parameters and sensors to be used therefor are well known. Table 1 provides several examples of such well known methods and shows the parameter in question, the method used, the sensor device used, and the signal that is generated. Table 1 also provides an indication as to whether further processing based on the generated signal is required to generate the data.
It is to be specifically noted that a number of other types and categories of sensors may be utilized alone or in conjunction with those given above, including but not limited to relative and global positioning sensors for determination of motion or location of the user; torque & rotational acceleration for determination of orientation in space; blood chemistry sensors; interstitial fluid chemistry sensors; bio-impedance sensors; and several contextual sensors, such as: pollen, humidity, ozone, acoustic, body and ambient noise and sensors adapted to utilize the device in a biofingerprinting scheme.
The types of data listed in Table 1 are intended to be examples of the types of data that can be generated by the monitoring device. It is to be understood that other types of data relating to other parameters can be generated without departing from the scope of the present invention. Additionally, certain information may be derived from the above data, relating to an individual's physiological state. Table 2 provides examples of the type of information that can be derived, and indicates some of the types of data that can be used therefor.
Additionally, the device may also generate data indicative of various contextual parameters such as activity states or other data relating to the environment surrounding the individual. For example, air quality, sound level/quality, light quality or ambient temperature near the individual, or even the global positioning of the individual.
In order to derive information from the sensors and data types herein, a series of algorithms are developed for predicting user characteristics, continual measurements, durative contexts, instantaneous events, and cumulative conditions. User characteristics include permanent and semi-permanent parameters of the wearer, including aspects such as weight, height, and wearer identity. An example of a continual measurement is energy expenditure, which constantly measures, for example on a minute by minute basis, the number of calories of energy expended by the wearer. Durative contexts are behaviors that last some period of time, such as sleeping, driving a car, or jogging. Instantaneous events are those that occur at a fixed or over a very short time period, such as a heart attack or falling down. Cumulative conditions are those where the person's condition can be deduced from their behavior over some previous period of time. For example, if a person hasn't slept in 36 hours and hasn't eaten in 10 hours, it is likely that they are fatigued. Table 3 below shows numerous examples of specific personal characteristics, continual measurements, durative measurements, instantaneous events, and cumulative conditions.
It will be appreciated that the present invention may be utilized in a method for doing automatic journaling of a wearer's physiological and contextual states. The system can automatically produce a journal of what activities the user was engaged in, what events occurred, how the user's physiological state changed over time, and when the user experienced or was likely to experience certain conditions. For example, the system can produce a record of when the user exercised, drove a car, slept, was in danger of heat stress, or ate, in addition to recording the user's hydration level, energy expenditure level, sleep levels, and alertness levels throughout a day. These detected conditions can be utilized to time- or event-stamp the data record, to modify certain parameters of the analysis or presentation of the data, as well as trigger certain delayed or real time feedback events.
According to the algorithm development process, linear or non-linear mathematical models or algorithms are constructed that map the data from the plurality of sensors to a desired variable. The process consists of several steps. First, data is collected by subjects wearing armband body monitoring device 300 who are put into situations as close to real world situations as possible, with respect to the parameters being measured, such that the subjects are not endangered and so that the variable that the proposed algorithm is to predict can, at the same time, be reliably measured using, for example, highly accurate medical grade lab equipment. This first step provides the following two sets of data that are then used as inputs to the algorithm development process: (i) the raw data from armband body monitoring device 300, and (ii) the data consisting of the verifiably accurate data measurements and extrapolated or derived data made with or calculated from the more accurate lab equipment. This verifiable data becomes a standard against which other analytical or measured data is compared. For cases in which the variable that the proposed algorithm is to predict relates to context detection, such as traveling in a motor vehicle, the verifiable standard data is provided by the subjects themselves, such as through information input manually into armband body monitoring device 300, a PC, or otherwise manually recorded. The collected data, i.e., both the raw data and the corresponding verifiable standard data, is then organized into a database and is split into training and test sets.
Next, using the data in the training set, a mathematical model is built that relates the raw data to the corresponding verifiable standard data. Specifically, a variety of machine learning techniques are used to generate two types of algorithms: 1) algorithms known as features, which are derived continuous parameters that vary in a manner that allows the prediction of the lab-measured parameter for some subset of the data points. The features are typically not conditionally independent of the lab-measured parameter e.g. VO2 level information from a metabolic cart, douglas bag, or doubly labeled water, and 2) algorithms known as context detectors that predict various contexts, e.g., running, exercising, lying down, sleeping or driving, useful for the overall algorithm. A number of well known machine learning techniques may be used in this step, including artificial neural nets, decision trees, memory-based methods, boosting, attribute selection through cross-validation, and stochastic search methods such as simulated annealing and evolutionary computation.
After a suitable set of features and context detectors are found, several well known machine learning methods are used to combine the features and context detectors into an overall model. Techniques used in this phase include, but are not limited to, multilinear regression, locally weighted regression, decision trees, artificial neural networks, stochastic search methods, support vector machines, and model trees. These models are evaluated using cross-validation to avoid over-fitting.
At this stage, the models make predictions on, for example, a minute by minute basis. Inter-minute effects are next taken into account by creating an overall model that integrates the minute by minute predictions. A well known or custom windowing and threshold optimization tool may be used in this step to take advantage of the temporal continuity of the data. Finally, the model's performance can be evaluated on the test set, which has not yet been used in the creation of the algorithm. Performance of the model on the test set is thus a good estimate of the algorithm's expected performance on other unseen data. Finally, the algorithm may undergo live testing on new data for further validation.
Further examples of the types of non-linear functions and/or machine learning method that may be used in the present invention include the following: conditionals, case statements, logical processing, probabilistic or logical inference, neural network processing, kernel based methods, memory-based lookup including kNN and SOMs, decision lists, decision-tree prediction, support vector machine prediction, clustering, boosted methods, cascade-correlation, Boltzmann classifiers, regression trees, case-based reasoning, Gaussians, Bayes nets, dynamic Bayesian networks, HMMs, Kalman filters, Gaussian processes and algorithmic predictors, e.g. learned by evolutionary computation or other program synthesis tools.
Although one can view an algorithm as taking raw sensor values or signals as input, performing computation, and then producing a desired output, it is useful in one preferred embodiment to view the algorithm as a series of derivations that are applied to the raw sensor values. Each derivation produces a signal referred to as a derived channel. The raw sensor values or signals are also referred to as channels, specifically raw channels rather than derived channels. These derivations, also referred to as functions, can be simple or complex but are applied in a predetermined order on the raw values and, possibly, on already existing derived channels. The first derivation must, of course, only take as input raw sensor signals and other available baseline information such as manually entered data and demographic information about the subject, but subsequent derivations can take as input previously derived channels. Note that one can easily determine, from the order of application of derivations, the particular channels utilized to derive a given derived channel. Also note that inputs that a user provides on an Input/Output, or I/O, device or in some fashion can also be included as raw signals which can be used by the algorithms. For example, the category chosen to describe a meal can be used by a derivation that computes the caloric estimate for the meal. In one embodiment, the raw signals are first summarized into channels that are sufficient for later derivations and can be efficiently stored. These channels include derivations such as summation, summation of differences, and averages. Note that although summarizing the high-rate data into compressed channels is useful both for compression and for storing useful features, it may be useful to store some or all segments of high rate data as well, depending on the exact details of the application. In one embodiment, these summary channels are then calibrated to take minor measurable differences in manufacturing into account and to result in values in the appropriate scale and in the correct units. For example, if, during the manufacturing process, a particular temperature sensor was determined to have a slight offset, this offset can be applied, resulting in a derived channel expressing temperature in degrees Celsius.
For purposes of this description, a derivation or function is linear if it is expressed as a weighted combination of its inputs together with some offset. For example, if G and H are two raw or derived channels, then all derivations of the form A*G+B*H+C, where A, B, and C are constants, is a linear derivation. A derivation is non-linear with respect to its inputs if it can not be expressed as a weighted sum of the inputs with a constant offset. An example of a nonlinear derivation is as follows: if G>7 then return H*9, else return H*3.5+912. A channel is linearly derived if all derivations involved in computing it are linear, and a channel is nonlinearly derived if any of the derivations used in creating it are nonlinear. A channel nonlinearly mediates a derivation if changes in the value of the channel change the computation performed in the derivation, keeping all other inputs to the derivation constant.
According to a preferred embodiment of the present invention, the algorithms that are developed using this process will have the format shown conceptually in
Referring to
As another example, an algorithm having the format shown conceptually in
Other important feedback embodiments include the ability to detect REM sleep through the heart related parameters and to maximize the wearer's opportunity to engage in such sleep. Rather than the conventional alarm waking the user at a preappointed time, the alarm could wake the wearer after a preset amount of REM sleep, and further at an appropriate endpoint of such sleep or during or just after some particular sleep stage.
This algorithm development process may also be used to create algorithms to enable armband body monitoring device 300 to detect and measure various other parameters, including, without limitation, the following: (i) when an individual is suffering from duress, including states of unconsciousness, fatigue, shock, drowsiness, heat stress and dehydration; and (ii) an individual's state of readiness, health and/or metabolic status, such as in a military environment, including states of dehydration, under-nourishment and lack of sleep. In addition, algorithms may be developed for other purposes, such as filtering, signal clean-up and noise cancellation for signals measured by a sensor device as described herein. As will be appreciated, the actual algorithm or function that is developed using this method will be highly dependent on the specifics of the sensor device used, such as the specific sensors and placement thereof and the overall structure and geometry of the sensor device. Thus, an algorithm developed with one sensor device will not work as well, if at all, on sensor devices that are not substantially structurally identical to the sensor device used to create the algorithm.
It is to be specifically understood that the method for creation of algorithms described above can be applied utilizing the detected signal from the apparatus as input to provide a methodology for beat detection. The detected signal is treated as a channel, as described above and the same techniques are applied.
Another aspect of the present invention relates to the ability of the developed algorithms to handle various kinds of uncertainty. Data uncertainty refers to sensor noise and possible sensor failures. Data uncertainty is when one cannot fully trust the data. Under such conditions, for example, if a sensor, for example an accelerometer, fails, the system might conclude that the wearer is sleeping or resting or that no motion is taking place. Under such conditions it is very hard to conclude if the data is bad or if the model that is predicting and making the conclusion is wrong. When an application involves both model and data uncertainties, it is very important to identify the relative magnitudes of the uncertainties associated with data and the model. An intelligent system would notice that the sensor seems to be producing erroneous data and would either switch to alternate algorithms or would, in some cases, be able to fill the gaps intelligently before making any predictions. When neither of these recovery techniques are possible, as was mentioned before, returning a clear statement that an accurate value can not be returned is often much preferable to returning information from an algorithm that has been determined to be likely to be wrong. Determining when sensors have failed and when data channels are no longer reliable is a non-trivial task because a failed sensor can sometimes result in readings that may seem consistent with some of the other sensors and the data can also fall within the normal operating range of the sensor.
Clinical uncertainty refers to the fact that different sensors might indicate seemingly contradictory conclusions. Clinical uncertainty is when one cannot be sure of the conclusion that is drawn from the data. For example, the accelerometers might indicate that the wearer is motionless, leading toward a conclusion of a resting user, the galvanic skin response sensor might provide a very high response, leading toward a conclusion of an active user, the heat flow sensor might indicate that the wearer is still dispersing substantial heat, leading toward a conclusion of an active user, and the heart rate sensor might indicate that the wearer has an elevated heart rate, leading toward a conclusion of an active user. An inferior system might simply try to vote among the sensors or use similarly unfounded methods to integrate the various readings. The present invention weights the important joint probabilities and determines the appropriate most likely conclusion, which might be, for this example, that the wearer is currently performing or has recently performed a low motion activity such as stationary biking.
This same algorithm development process was used to develop the algorithms disclosed above for detecting heart beats, for determining heart rate, and for estimating heart rate in the presence of noise. It will be clear to one skilled in the art that this same process could be utilized to both incorporate other sensors to improve the measurement of heart related parameters or to incorporate heart related parameters into the measurement of other physiological parameters such as energy expenditure.
According to a further aspect of the present invention, a sensor device such as armband body monitoring device 300 may be used to automatically measure, record, store and/or report a parameter Y relating to the state of a person, preferably a state of the person that cannot be directly measured by the sensors. State parameter Y may be, for example and without limitation, calories consumed, energy expenditure, sleep states, hydration levels, ketosis levels, shock, insulin levels, physical exhaustion and heat exhaustion, among others. The sensor device is able to observe a vector of raw signals consisting of the outputs of certain of the one or more sensors, which may include all of such sensors or a subset of such sensors. As described above, certain signals, referred to as channels same potential terminology problem here as well, may be derived from the vector of raw sensor signals as well. A vector X of certain of these raw and/or derived channels, referred to herein as the raw and derived channels X, will change in some systematic way depending on or sensitive to the state, event and/or level of either the state parameter Y that is of interest or some indicator of Y, referred to as U, wherein there is a relationship between Y and U such that Y can be obtained from U. According to the present invention, a first algorithm or function f1 is created using the sensor device that takes as inputs the raw and derived channels X and gives an output that predicts and is conditionally dependent, expressed with the symbol on (i) either the state parameter Y or the indicator U, and (ii) some other state parameter(s) Z of the individual. This algorithm or function f1 may be expressed as follows:
f1(X)U+Z
or
f1(X)Y+Z
According to the preferred embodiment, f1 is developed using the algorithm development process described elsewhere herein which uses data, specifically the raw and derived channels X, derived from the signals collected by the sensor device, the verifiable standard data relating to U or Y and Z contemporaneously measured using a method taken to be the correct answer, for example highly accurate medical grade lab equipment, and various machine learning techniques to generate the algorithms from the collected data. The algorithm or function f1 is created under conditions where the indicator U or state parameter Y, whichever the case may be, is present. As will be appreciated, the actual algorithm or function that is developed using this method will be highly dependent on the specifics of the sensor device used, such as the specific sensors and placement thereof and the overall structure and geometry of the sensor device. Thus, an algorithm developed with one sensor device will not work as well, if at all, on sensor devices that are not substantially structurally identical to the sensor device used to create the algorithm or at least can be translated from device to device or sensor to sensor with known conversion parameters.
Next, a second algorithm or function f2 is created using the sensor device that takes as inputs the raw and derived channels X and gives an output that predicts and is conditionally dependent on everything output by f1 except either Y or U, whichever the case may be, and is conditionally independent, indicated by the symbol , of either Y or U, whichever the case may be. The idea is that certain of the raw and derived channels X from the one or more sensors make it possible to explain away or filter out changes in the raw and derived channels X coming from non-Y or non-U related events. This algorithm or function f2 may be expressed as follows:
f2(X)Z and (f2(X)Y or f2(X)U
Preferably, f2, like f1, is developed using the algorithm development process referenced above. f2, however, is developed and validated under conditions where U or Y, whichever the case may, is not present. Thus, the gold standard data used to create f2 is data relating to Z only measured using highly accurate medical grade lab equipment.
Thus, according to this aspect of the invention, two functions will have been created, one of which, f1, is sensitive to U or Y, the other of which, f2, is insensitive to U or Y. As will be appreciated, there is a relationship between f1 and f2 that will yield either U or Y, whichever the case may be. In other words, there is a function f3 such that f3 (f1, f2)=U or B (f1, f2)=Y. For example, U or Y may be obtained by subtracting the data produced by the two functions (U=f1−f2 or Y=f1−f2). In the case where U, rather than Y, is determined from the relationship between f1 and f2, the next step involves obtaining Y from U based on the relationship between Y and U. For example, Y may be some fixed percentage of U such that Y can be obtained by dividing U by some factor.
One skilled in the art will appreciate that in the present invention, more than two such functions, e.g. (f1, f2, f3, . . . f_n−1) could be combined by a last function f_n in the manner described above. In general, this aspect of the invention requires that a set of functions is combined whose outputs vary from one another in a way that is indicative of the parameter of interest. It will also be appreciated that conditional dependence or independence as used here will be defined to be approximate rather than precise. For example, it is known that total body metabolism is measured as total energy expenditure, or TEE, according to the following equation:
TEE=BMR+AE+TEF+AT,
wherein BMR is basal metabolic rate, which is the energy expended by the body during rest such as sleep, AE is activity energy expenditure, which is the energy expended during physical activity, TEF is thermic effect of food, which is the energy expended while digesting and processing the food that is eaten, and AT is adaptive thermogenesis, which is a mechanism by which the body modifies its metabolism to extreme temperatures. It is estimated that it costs humans about 10% of the value of food that is eaten to process the food. TEF is therefore estimated to be 10% of the total calories consumed. Thus, a reliable and practical method of measuring TEF would enable caloric consumption to be measured without the need to manually track or record food related information. Specifically, once TEF is measured, caloric consumption can be accurately estimated by dividing TEF by 0.1 (TEF=0.1*Calories Consumed; Calories Consumed=TEF/0.1).
According to a specific embodiment of the present invention relating to the automatic measurement of a state parameter Y as described above, a sensor device as described above may be used to automatically measure and/or record calories consumed by an individual. In this embodiment, the state parameter Y is calories consumed by the individual and the indicator U is TEF. First, the sensor device is used to create f1, which is an algorithm for predicting TEE. f1 is developed and validated on subjects who ate food, in other words, subjects who were performing activity and who were experiencing a TEF effect. As such, f1 is referred to as EE(gorge) to represent that it predicts energy expenditure including eating effects. The verifiable standard data used to create f1 is a VO2 machine. The function f1, which predicts TEE, is conditionally dependent on and predicts the item U of interest, which is TEF. In addition, f1 is conditionally dependent on and predicts Z which, in this case, is BMR+AE+AT. Next, the sensor device is used to create f2, which is an algorithm for predicting all aspects of TEE except for TEF. f2 is developed and validated on subjects who fasted for a period of time prior to the collection of data, preferably 4-6 hours, to ensure that TEF was not present and was not a factor. Such subjects will be performing physical activity without any TEF effect. As a result, f2 is conditionally dependent to and predicts BMR+AE+AT but is conditionally independent of and does not predict TEF. As such, f2 is referred to as EE(fast) to represent that it predicts energy expenditure not including eating effects. Thus, f1 so developed will be sensitive to TEF and f2 so developed will be insensitive to TEF. As will be appreciated, in this embodiment, the relationship between f1 and f2 that will yield the indicator U, which in this case is TEF, is subtraction. In other words, EE (gorge)−EE (fast)=TEF.
In the most preferred embodiment, armband body monitoring device 300 includes and/or is in communication with a body motion sensor such as an accelerometer adapted to generate data indicative of motion, a skin conductance sensor such as a GSR sensor adapted to generate data indicative of the resistance of the individual's skin to electrical current, a heat flux sensor adapted to generate data indicative of heat flow off the body, a electrodes for generating an ECG signal from which data indicative of the rate or other characteristics of the heart beats of the individual may be generated, and a temperature sensor adapted to generate data indicative of a temperature of the individual's skin. In this preferred embodiment, these signals, in addition the demographic information about the wearer, make up the vector of signals from which the raw and derived channels X are derived. Most preferably, this vector of signals includes data indicative of motion, resistance of the individual's skin to electrical current, heat flow off the body, and heart rate.
Another specific instantiation where the present invention can be utilized relates to detecting when a person is fatigued. Such detection can either be performed in at least two ways. A first way involves accurately measuring parameters such as their caloric intake, hydration levels, sleep, stress, and energy expenditure levels using a sensor device and using the two function (f1 and f2) approach to provide an estimate of fatigue. A second way involves directly attempting to model fatigue using the direct derivational approach described in connection with
It will be appreciated that algorithms can use both calibrated sensor values and complex derived algorithms. This is effective in predicting endpoints to or thresholds of certain physiological conditions and informing the wearer or other observer of an approximate measure of time or other activity until the endpoint is likely to be reached.
Another application of the current invention is as a component in an apparatus for doing wearer fingerprinting and authentication. A 128-Hz heart-rate signal is a rich signal, and personal characteristics such as resting heart rate, beat to beat variability, response to stimuli, and fitness will show up in the signal. These identifying personal characteristics can be used to verify that the wearer is indeed the approved wearer for the device or to identify which of a range of possible approved wearers is currently wearing the device. In one embodiment of this aspect of the invention, only the 128-hz signal and derived parameters from that signal are utilized for identification. In another, all of the sensors in the monitor are used together as inputs for the identification algorithm.
In another application of this aspect of the invention, an authentication armband can be utilized in a military or first responder system as a component in a friend or foe recognition system.
Interaction with other devices is also contemplated. The system can augment the senses and also the intelligence of other products and computer systems. This allows the associated devices to collectively know more about their user and be able to react appropriately, such as automatically turning the thermostat in the house up or down when asleep or turning the lights on when awakened. In the entertainment context, the detection of certain stress and heart related parameters may be utilized to affect sound, light and other effects in a game, movie or other type of interactive entertainment. Additionally, the user's condition may be utilized to alter musical programming, such as to increase the tempo of the music coincident with the changing heart rate of the user during exercise or meditation. Further examples include turning the car radio down when the person gets stressed while they drive because they're looking for an address; causing an appliance to prepare a caffeinated drink when the person is tired; matching up people in a social environment in the same mood or with the same tastes; utilizing alertness and stress indicators to tune teaching systems such as intelligent tutors or flight simulators, to maximize the student's progress; removing a person's privileges or giving a person privileges based on their body state, for example not letting a trucker start up his truck again until he has had 8 hours of sleep; providing automatic login to systems such as the wearer's personal computer based on biometric fingerprinting; and creating new user interfaces guided in part or in whole by gross body states for impaired individuals such as autistic children.
Moreover, new human-computer interactions can be envisioned that use bio-states to adjust how the computer reacts to the person. For example, a person is tele-operating a robotic arm. The system can see he is tired and so smoothes out some of his motion to adjust for some expected jerkiness due to his fatigue.
Individuals with suspected heart rhythm irregularities will often undergo some type of home or ambulatory ECG monitoring. Quite often, the individual's symptoms appear infrequently and irregularly, such as one a day, once a week, once a month, or even less often. In such cases, it is unlikely that the symptoms will be detected during a visit to the doctor in which classic ECG measurements are taken. Thus the need for home or ambulatory ECG monitoring to attempt to capture such infrequent episodes. The most common home or ambulatory ECG monitoring methods are Holter monitoring, event recording, and continuous loop recording, as described above.
According to another aspect of the present invention, a device as described herein that measures an ECG signal may be adapted and configured to perform the functionality of a Holter monitor, an event recorder, or a continuous loop recorder. Preferably, such a device may be armband body monitoring device 300 as illustrated and described herein. Such a device may be comfortably worn for extended periods of time, unlike a Holter monitor or an event recorder on a convenient location on a limb, such as the upper arm in the case of armband body monitoring device 300. In addition, the recorded ECG signals may be combined with other data that is contemporaneously measured by such a device in accordance with other aspects of the present invention described herein, including the various physiological parameters and/or contexts that may be predicted and measured using the algorithms described herein, to provide automatically context and/or parameter annotated heart related information. For example, as shown in
It is also well known that there is a circadian pattern to certain arrhythmias or conditions which lead to heart related stress. Sudden cardiac arrest, for example, has a high incidence in early morning. It is therefore anticipated that the detection might be enhanced during certain time periods, or that other devices could be cued by the monitoring system to avoid certain coincident or inappropriate activities or interactions. A pacemaker, for example could raise pace according to a preset protocol as the wearer comes out of sleep or waking the user calmly at the end of a REM stage of sleep.
The system is further applicable in diagnostic settings, such as the calibration of drug therapies, post-surgical or rehabilitative environments or drug delivery monitoring, with immediate and real time effects of these medical applications and procedures being monitored continuously and non-invasively.
This type of application may also be utilized in a mass emergency or other crisis situation, with victims being collected in one location (for example a gymnasium) and are being seen by nurses, EMTs, physicians, volunteers—where this staff is basically short staffed for this type of situation and diagnosing or keeping watchful monitoring over all the victims now patients (some quite injured and others under observation in case the injury or shock are delayed in terms of physical/tactile/visual symptoms). A system having diagnostic heart related capabilities and, optionally, hydration, hypothermia, stress or shock could be distributed upon each victim's entrance for monitoring. The design of the system, which alleviates the need to remove most clothing for monitoring, would both speed and ease the ability of the caregivers to apply the devices. This system could send the alerts to a central system in the facility where the serial number is highlighted, and the attendant is alerted that a condition has been triggered, the nature of the condition as well as the priority. Within this collaborative armband scenario, all the armbands around the condition sensing/triggering armband could also beep or signal differently to focus the attention of an attendant to that direction more easily. Additionally, certain techniques, as described below, would allow all of the armbands to interactively coordinate and validate their relative location continuously with the surrounding armbands, allowing the central monitoring station to locate where in the facility the location of any particular armband is located and where specifically are the individuals who need the most immediate attention.
More specifically, the device could be designed to be part of a network of devices solving as a network of devices the exact or relative locations of each device in the network. In this embodiment each device would have one or more mechanisms for determining the relative position of itself to another device in the network. Examples of how this could be done include the sending of RF, IR, or acoustic signals between the devices and using some technique such as time of flight and/or phase shifts to determine the distance between the devices. It is a known problem that methods such as these are prone to errors under real world circumstances and in some cases, such as the phase shift method, give the receiving device an infinite number of periodic solutions to the relative distance question. It is also typical that such devices, because of power limitations, occasional interference from the environment and the like, would lose and then later regain contact with other devices in the networks so that at any one time each device might only have communication with a subset of the other devices in the network.
Given this ability to establish at each moment in time a relative distance between each pair of devices, and the ability of the devices to share what they know with all other devices in the network, for a network for N devices, there are a total of (N*(N−1))/2 distances to be measured and it is practical that every device could, by passing on all they know to all the devices they can communicate with at that moment in time, arrive at a state where all devices in the network have all available relative distances that could be measured, which would be some subset of the (N*(N−1))/2 possible distances to be measured, and could have updates to this list of numbers quite often, e.g. several times per minute, relative to the speed at which the wearers are changing relative to each other.
Once each device has a list of these distances, each device effectively has a system of equations and unknowns. For example: A is approximately X meters from B, B is approximately Y meters from C, C is approximately Z meters from A, A is U meters from D, B is T meters from D, C is V meters from D. Alternatively, under the phase shift only model, these equations could be as follows: A is some integer multiple of six inches from B, B is some integer multiple of eight inches from C, C is some integer multiple of one foot from D, and D is some integer multiple of seven inches from A. To the extent there is redundant information in the network, as in the examples just given, and with the possible additional assumptions about the topology on which the wearers are situated, such as a flat area, a hill that rises/falls no faster than a grade of 6% or the like, each device can solve this system of equations and unknowns or equations and inaccurate values to significantly refine the estimates of the distance between each pair of devices. These results can be then shared between devices so that all devices have the most accurate, up-to-date information and all agree, at each moment in time, what their relative positions are. This solving of equations can be done through a process such as dynamic programming or a matrix solution form such as singular value decomposition. The previous values each wearer's device has for its distance to all the other devices can be included in these calculations as follows to take advantage for things such as if A was ten feet from B five seconds ago, it is highly unlikely that A is now two hundred feet from B even if that is one of the possible solutions to the system of equations and unknowns.
An alternative embodiment involves utilizing probabilistic reasoning to keep track of a probabilistic estimate of the relative location of each wearer and for taking into account possible sensor noise and expected motion. Kalman filters are an example of this sort of reasoning often applied in tracking a single moving entity; extensions to multiple interacting entities are available.
If these devices are also equipped with ability to know or be told, from time to time, their actual or approximate global location, such as through an embedded GPS chip, then this information could also be shared with all the other devices in the network so that, adjusting for their relative distances, each device will then know its global location.
To aid in this process, it is preferred that there be provided at least one interval where the relative positions are known for the entire network. This, along with frequent updates, relative to the rate they move relative to each other, to the relative distances of the devices, reduces the possibly solutions for these systems of equations and thereby improves the accuracy of the process. This synchronization of the devices could be accomplished for example, for having them together in the identical location for a moment before each devices sets out on its own for a time.
Referring to
Elastic strap 309 is used to removably affix armband body monitoring device 300 to the individual's upper arm. The surface of elastic strap is provided with velcro loops along a portion thereof. Each end of elastic strap 309 is provided with a velcro hook patch on the bottom surface and a pull tab. A portion of each pull tab extends beyond the edge of each end 427.
An activation button 314 is provided for appropriate user input, while LED output indicators 316 provide context-sensitive output. In particular, circuit 200 is provided inside housing 305 of armband body monitoring device 300, and the various electrodes and sensors identified herein are electrically connected thereto, as will be apparent to one skilled in the art. CPU 165 of circuit 200 would, in this embodiment, preferably be the processing unit forming part of the armband body monitoring device circuitry described in U.S. Pat. No. 6,605,038 and U.S. application Ser. No. 10/682,293, the specifications of both which are hereby incorporated by reference.
Referring now to
Armband body monitoring device 300 is designed to be worn on the back of the upper arm, in particular on the triceps muscle of the upper arm, most preferably the left arm. Referring to the specific embodiment shown in
Referring now to
With reference to
An additional aspect of the embodiments illustrated herein is the opportunity to select certain aspects of each device and place the same in disposable segments of the device, as illustrated with particularity in
The ECG wave form collected from inside any of the equivalence class regions will not necessarily have the shape of a standard ECG wave form. When this is the case, a mapping can be created between a ECG wave form taken within a single equivalence class region and ECG wave forms taken between equivalence class regions. This can be done using the algorithm development process described above, creating a function that warps the within equivalence class region to be clearer when displayed as a standard ECG wave form.
Although particular embodiments of the present invention have been illustrated in the accompanying drawings and described in the foregoing detailed description, it is to be further understood that the present invention is not to be limited to just the embodiments disclosed, but that they are capable of numerous rearrangements, modifications and substitutions, as identified in the following claims.
Claims
1. A method for monitoring human heart related status parameters, comprising:
- locating inequipotential heart related signal locations on the human body within an equivalence region of said body;
- placing a sensor having at least two electrodes on said body, said electrodes mounted to engage said signal locations;
- detecting a heart related signal indicative of said heart related status parameters from said electrodes; and
- processing said signal to extract said heart related parameters from said signal.
Type: Application
Filed: Oct 30, 2007
Publication Date: Jul 24, 2008
Inventors: Jonathan FARRINGDON (PITTSBURGH, PA), JOHN M. STIVORIC (PITTSBURGH, PA), ERIC TELLER (PITTSBURGH, PA), DAVID ANDRE (PITTSBURGH, PA), SCOTT K. BOEHMKE (WEXFORD, PA), JAMES GASBARRO (FOX CHAPEL, PA), GREGORY KOVACS (PALO ALTO, CA), RAYMOND PELLETIER (PITTSBURGH, PA), CHRISTOPHER KASABACH (PITTSBURGH, PA)
Application Number: 11/928,333
International Classification: A61B 5/0402 (20060101);
