SYSTEM AND METHOD FOR IMPROVED OBSTRUCTIVE SLEEP APNEA DIAGNOSTIC FOR IMPLANTABLE DEVICES

Abstract

A system and method of diagnosing sleep apnea including an implantable device with a sensor, a telemetry circuit and a memory, an external programmer in communication with the telemetry circuit and configured to receive data collected by the sensor and stored in the memory. The system and method include operation of a server, including a processor, in communication with the external programmer and storing an application including instructions that when executed by the processor executes steps of receiving the data collected by the sensor from the external programmer, analyzing the received data collected by the sensor, and transmitting to a remote computer an assessment of the received sensor data, wherein the assessment includes an evaluation of sleep apnea for the patient.

Description

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Application No. 62/814,398 filed Mar. 6, 2019 and entitled INTRAMUSCULAR HYPOGLOSSAL NERVE STIMULATION FOR OBSTRUCTIVE SLEEP APNEA THERAPY, the entire contents of which are incorporated herein by reference.

TECHNICAL FIELD

This disclosure relates to implantable medical device systems and methods of assessing physiological data to diagnose sleep apnea.

BACKGROUND

Implantable medical devices capable of delivering electrical stimulation pulses have been proposed or are available for treating a variety of medical conditions, such as cardiac arrhythmias and chronic pain as examples. Sleep apnea is generally separated into two forms obstructive sleep apnea (OSA) and central sleep apnea (CSA). Sleep apnea is a serious disorder in which breathing is irregularly and repeatedly stopped and started during sleep, resulting in disrupted sleep and reducing blood oxygen levels. OSA is caused by complete or partial collapse of the pharynx during sleep. In particular, muscles in a patient's mouth and throat intermittently relax thereby obstructing the upper airway while sleeping. Airflow into the upper airway can be obstructed by the tongue or soft pallet moving to the back of the throat and covering a smaller than normal airway. Loss of air flow also causes unusual inter-thoracic pressure as a person tries to breathe with a blocked airway. In contrast CSA is generally the result of the cessation of respiratory drive. That is, the brain fails to provide the necessary signals to your diaphragm and other muscles to engage in breathing. Regardless in both OSA and CSA Lack of adequate levels of oxygen during sleep can contribute to abnormal heart rhythms, heart attack, heart failure, high blood pressure, stroke, memory problems and increased accidents. Indeed, sleep apnea has a high rate of co-morbidity with many forms of heart disease and particularly cardiac rhythm disease. Additionally, loss of sleep occurs when a person is awakened during an apneic episode.

SUMMARY

One aspect of the disclosure is directed to a method of assessing a patient for sleep apnea including: receiving sensor data from a device implanted in a patient for treatment of a heart related disease at an external programmer; transmitting the received sensor data to a remote server; analyzing the received data at the server; and transmitting to a remote computer an assessment of the received sensor data, where the assessment includes an evaluation of sleep apnea for the patient. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods and systems described herein.

Implementations of this aspect of the disclosure may include one or more of the following features. The method further including receiving sensor data from an external sensor. The method further including receiving self-reported data. The method where the sensor data includes data indicative of the posture of the patient, motion data of the patient, electroencephalogram (EEG) data, electrocardiogram (ECG) data, apnea hypopnea index (AHI) data or blood-oxygen saturation data. The method where the sensor data includes data from one or more three-axis accelerometers. The method where the sensor data includes respiration rate data, heart rate data, total sleep time data, sleep efficiency data, sleep stage data, arousals data, or awakenings data. The method where the implanted device selected from the group including of a pacemaker, an implantable cardiac defibrillators (ICD), a cardiac resynchronization therapy (CRT) device, and an implantable neurostimulator (INS). The method further including a neural network performing the analysis of the received data. The system further including one or more external sensors configured to transmit sensor data to the server or the external programmer. The system further including a user-interface presented on the external programmer and configured to receive self-reported data. The system where the sensor outputs data indicative of the posture of the patient, motion of the patient, an electroencephalogram (EEG), an electrocardiogram (ECG), an apnea hypopnea index (AHI) or blood-oxygen saturation. The system where the sensor is included of one or more three-axis accelerometers. The system where the sensor outputs data indicative of respiration rate, heart rate, total sleep time, sleep efficiency, sleep stage, arousals, or awakenings. The system where the implanted device selected from the group including of a pacemaker, an implantable cardiac defibrillators (ICD), a cardiac resynchronization therapy (CRT) device, and an implantable neurostimulator (INS). The system further including a neural network performing the analysis of the received data at the server,

A further aspect of the disclosure is directed to a system including: an implantable device including a sensor, a telemetry circuit and a memory; an external programmer in communication with the telemetry circuit and configured to receive data collected by the sensor and stored in the memory; a server, including a processor, in communication with the external programmer and storing thereon an application including Instructions that when executed by the processor executes steps of. The system also includes receiving the data collected by the sensor from the external programmer. The system also includes analyzing the received data collected by the sensor. The system also includes transmitting to a remote computer an assessment of the received sensor data, where the assessment includes an evaluation of sleep apnea for patient in which the implantable device has been implanted. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods and systems described herein.

Yet a further aspect of the disclosure is directed to a computer readable recording medium storing thereon instructions that when executed by a processor and cause the processor to execute the steps of: receiving sensor data from an external processor, the sensor data having been collected by an implanted device configured for treatment of a heart related disease; analyzing the received sensor data; and transmitting to a remote computer an assessment of the received sensor data, where the assessment includes an evaluation of sleep apnea for a patient in which the implantable device has been implanted. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods and systems described herein.

Implementations of this aspect of the disclosure may include one or more of the following features. The computer readable recording medium where the sensor data includes data indicative of the posture of the patient, motion data of the patient, electroencephalogram (EEG) data, electrocardiogram (ECG) data, apnea hypopnea index (AHI) data or blood-oxygen saturation data. The computer readable recording medium where the sensor data includes data from one or more three-axis accelerometers. The computer readable recording medium where the sensor data includes respiration rate data, heart rate data, total sleep time data, sleep efficiency data, sleep stage data, arousals data, or awakenings data. Implementations of the described techniques may include hardware, a method or process, or computer software on a computer-accessible medium, including software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a conceptual diagram of an implantable device in accordance with one aspect of the disclosure;

FIG. 2 is a conceptual diagram of a system in accordance with the present disclosure; and

FIG. 3 depicts a flow chart for collecting, transmitting, and analyzing data derived from the implantable device of FIG. 1.

DETAILED DESCRIPTION

This disclosure is directed to systems and methods of improved sleep apnea diagnosis and monitoring using data collected from implanted devices.

As noted above, many forms of heart disease are co-morbid with sleep apnea. Typically sleep apnea is diagnosed using a sleep study or polysomnography (PSG). A typical PSG requires at least six hours of data collection and is usually undertaken in a clinical environment. As part of a PSG a variety of data is collected including an electroencephalogram (EEG) data (brain wave activity), an electroculogram (EOG) data (measuring eye and chin movements), electrocardiogram (ECG) data (heart rate and rhythm), respiration rate data, and blood oxygen saturation level data, airflow from the nose and mouth, and leg movement data, and snoring data. From these data further observations and determinations can be made including total sleep time (TST), sleep efficiency and latency (total sleep time compared to total recording time), sleep states, number of arousals (wakefulness less than 15 seconds), awakenings (wakefulness greater than 15 seconds), an Apnea Hypopnea Index (AHI) value.

The AHI is the number of apneas or hypopneas recorded during the study per hour of sleep. It is generally expressed as the number of events per hour. Based on the AHI, the severity of OSA is classified as follows in Table 1:

TABLE 1
Severity      Events
None/Minimal: <5 per hour
Mild:         >5, but <15 per hour
Moderate:     ≥15, but <30 per hour
Severe:       ≥30 per hour

While such PSG testing is the gold standard for determining whether a patient suffers from sleep apnea, this testing has a number of disadvantages. First, the patient themselves may not have a clear understanding that that they suffer from sleep apnea and may not seek out testing. Indeed, one of the oft reported reasons that a patient seeks testing is due to pressure from a spouse or partner who themselves may be suffering from the patient's snoring and other sleep apnea activities. Second, while the testing is being constantly monitored and the sleep conditions are being monitored, the recording must necessarily be done in an environment that is unfamiliar to the patient (resulting is potentially biased results). Third, due to all of the equipment being employed, the variety of wires, electrodes, and sensors can be uncomfortable and result in poor sleep. Finally, this type of testing with all of the equipment involved, the need for constant monitoring, and the time and expertise necessary to analyze the data can be quite expensive.

A wide variety of implantable devices are employed in assessing and applying therapy to patients suffering from various conditions. Common implantable devices include pacemakers, an implantable cardiac defibrillators (ICD), and cardiac resynchronization therapy (CRT) devices. In the case of pacemakers and ICDs these may be either single or dual chamber devices. In addition, more recently there have been developed implantable neurostimulators such as those used for the treatment of OSA by delivering therapy directly to the lingual muscles of a patient's tongue. All these implantable devices include a variety of sensors to collect various physiological data from the patient. Utilization of the data generated by these implantable devices provides an improved and largely automated system and method of assessing sleep apnea in patients having these implantable devices and is described in greater detail below.

FIG. 1 is a schematic diagram of an implantable device 10 in accordance with the disclosure. Implantable device 10 includes a control circuit 20, memory 30, therapy delivery circuit 40, a sensor 50, telemetry circuit 60 and power source 70. Power source 70 may include one or more rechargeable or non-rechargeable batteries for supplying electrical current to each of the control circuit 20, memory 30, therapy delivery circuit 40, sensor 50 and telemetry circuit 60. While power source 70 is shown in communication only with control circuit 20 for the sake of clarity, it is to be understood that power source 70 provides power as needed to each of the circuits and components of implantable device 10 as needed. For example, power source 70 provides power to therapy delivery circuit 40 for generating electrical stimulation pulses.

Sensor 50 may include one or more separate sensors for monitoring a patient condition. These sensors may include one or accelerometers, inertial measurement units (IMU), fiber-Bragg gratings (e.g., shape sensors), optical sensors, acoustic sensors, pulse oximeters, and others without departing from the scope of the disclosure and as will be described in greater detail below.

The functional blocks shown in FIG. 1 represent functionality included in an implantable device 10 such as those described above. The implantable device 10 may include any discrete and/or integrated electronic circuit components that implement analog and/or digital circuits capable of producing the functions attributed to a pulse generator herein. The various components may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, state machine, or other suitable components or combinations of components that provide the described functionality. Providing software, hardware, and/or firmware to accomplish the described functionality in the context of any modern medical device system, given the disclosure herein, is within the abilities of one of skill in the art.

Control circuit 20 communicates, e.g., via a data bus, with memory 30, therapy delivery circuit 40, telemetry circuit 60 and sensor 50 to control the delivery of therapy other functions. As disclosed herein, control circuit 20 may pass control signals to therapy delivery circuit 40 to cause therapy delivery circuit 40 to deliver electrical stimulation pulses via electrodes 80a-80d according to a therapy protocol. Control circuit 20 may further be configured to pass therapy control signals to therapy delivery circuit 40 including stimulation pulse amplitude, stimulation pulse width, stimulation pulse number and frequency of a stimulation pulse train.

Memory 30 may store instructions for execution by a processor included in control circuit 20, stimulation control parameters, and other device-related or patient-related data. Control circuit 20 may retrieve therapy delivery control parameters and a therapy delivery protocol from memory 30 to enable control circuit 20 to pass control signals to therapy delivery circuit 40 for controlling therapy. Memory 30 may store historical data relating to therapy delivery for retrieval by a user via telemetry circuit 60. Therapy delivery data or information stored in memory 30 may include therapy control parameters used to deliver stimulation pulses as well as delivered therapy protocol(s), hours of therapy delivery or the like. Patient related data, such as that received from the sensor 50 signal may be stored in memory 30 for retrieval by a user or other system components as described in greater detail below.

Therapy delivery circuit 40 may include a charging circuit 42, an output circuit 44, and a switching circuit 46. Charging circuit 42 may include one or more holding capacitors that are charged using a multiple of the battery voltage of power source 70, for example. The holding capacitors are switchably connected to output circuit 44, which may include one or more output capacitors that are coupled to a selected bipolar electrode pair via switching circuit 46. The holding capacitor(s) are charged to a programmed pacing pulse voltage amplitude by charging circuit 42 and discharged across the output capacitor for a programmed pulse width. Charging circuit 42 may include capacitor charge pumps or an amplifier for the charge source to enable rapid recharging of holding capacitors included in charging circuit 42. Therapy delivery circuit 40 responds to control signals from control circuit 20 for generating and delivering trains of pulses as therapeutic pulses to the electrodes 80a-80d.

Output circuit 44 may be selectively coupled to bipolar pairs of electrodes 80a-80d via switching circuit 46. Switching circuit 46 may include one or more switches activated by timing signals received from control circuit 20. Electrodes 80a-80d may be selectively coupled to output circuit 44 in a time-varying manner to deliver stimulation to different portions of the protrusor muscles at different time to avoid fatigue, without requiring stimulation to be withheld completely. Switching circuit 46 may include a switch array, switch matrix, multiplexer, or any other type of switching device(s) suitable to selectively couple therapy delivery circuit 40 to electrodes 80a-80d.

Telemetry circuit 60 may be included to enable bidirectional communication with an external programmer 90. A user, such as the patient, may manually adjust therapy control parameter settings, e.g., as described in Medtronic's Patient Programmer Model 37642, incorporated by reference in its entirety. The patient may make limited programming changes such as small changes in pulse amplitude and pulse width. The patient may turn the therapy on and off or to set timers to turn the therapy on or off using external programmer 90 in wireless telemetric communication with telemetry circuit 60.

In other examples, a user, such as a clinician, may interacts with a user interface of an external programmer 90 to program implantable device 10 according to a desired therapy protocol. For example, a Physician Programmer Model 8840 available from Medtronic, Inc., Minneapolis, Minn., may be used by the physician to program the implantable device 10.

Programming of implantable device 10 may refer generally to the generation and transfer of commands, programs, or other information to control the operation of the implantable device 10. For example, external programmer 90 may transmit programs, parameter adjustments, program selections, group selections, or other information to control the operation of implantable device 10, e.g., by wireless telemetry. As one example, external programmer 90 may transmit parameter adjustments to support therapy changes. As another example, a user may select programs or program groups. A program may be characterized by an electrode combination, electrode polarities, voltage or current amplitude, pulse width, pulse rate, therapy duration, and/or pattern of electrode selection for delivering patterns of alternating portions of the protrusor muscles that are being stimulated. A group may be characterized by multiple programs that are delivered simultaneously or on an interleaved or rotating basis. These programs may adjust output parameters or turn the therapy on or off at different time intervals.

In some cases, external programmer 90 may be characterized as a physician or clinician programmer if it is primarily intended for use by a physician or clinician. In other cases, external programmer 90 may be characterized as a patient programmer if it is primarily intended for use by a patient. A patient programmer 90 is generally accessible to patient and, in many cases, may be a portable device that may accompany the patient throughout the patient's daily routine. In general, a physician or clinician programmer may support selection and generation of programs by a clinician for use by implantable device 10, whereas a patient programmer may support adjustment and selection of such programs by a patient during ordinary use.

External programmer 90 may present patient related and/or device related data retrieved from memory 30 via telemetry circuit 60. For example, the patient related data may be a variety of sensor data received from sensor 50 and stored in memory 40. These data may be presented on one or more user interfaces via a display found on the external programmer 90 or in communication with the external programmer.

As will be apparent to those of skill in the art, the sensor 50, which may of course be any number of separate sensors, is a significant aspect of the disclosure. For example, the sensor 50 may be a blood-oxygen saturation sensor. This may be an optical sensor and configured as either a reflectance blood-oxygen saturation sensor or a transmissive blood-oxygen saturation sensor. In the case of the transmissive blood-oxygen sensor a light source may be formed as part of a cuff designed to surround a blood vessel. A photodetector may be configured on an opposite side of the cuff from the light source. Other configurations of the blood-oxygen saturation sensor either within a body of the implantable device 10 or operably connected there are also considered within the scope of the disclosure. Indeed, in accordance with the disclosure, the blood-oxygen saturation sensor may be entirely separate from the implantable device 10 and simply an external sensor applied to the finger of the patient, but in communication with the external programmer 90.

A further sensor 50 may be a motion detector. The motion detector may be an accelerometer, for example a three-axis accelerometer. This motion detector may be tuned to detect motion caused by movement of the patient, motion caused by the beating of the heart (e.g., measuring the patient's pulse), or motion caused by respiration (operation of the lungs) and others. For example, the sensor 50 may be tuned to detect movement of the patient's legs. In accordance with one aspect of the disclosure this might be detected motion that which is inconsistent with heart rate movement or respiration movement and does not result in a change in posture of the patient. Still further, the three-axis accelerometer may be tuned to detect snoring. Again, band pass filtering can be employed to remove all but the high frequency input that is associated with snoring.

The sensor 50 may be a posture detector. As a posture detector, a three-axis accelerometer can be employed to detect when the patient is in a reclined or sleeping position and even whether the patient is laying prone or supine or laying on their right or left sides. The effect of 1G of gravitational acceleration applied directly along an axis of a stationary accelerometer provides a characteristic output voltage signal having an amplitude that can be referenced or scaled as +1 for angular computation purposes. The effect of 1 G of gravitational acceleration applied in precisely the opposite or negative direction to the sensitive axis provides a characteristic output voltage signal amplitude that is referenced or scaled as −1. If the axis is oriented transverse to the direction of the gravitational force, a bias voltage level output signal should be present, and that voltage signal level is referenced or scaled as 0. The degree to which the axis is oriented away or tilted from the direction of the gravitational force can also be detected by the magnitude and polarity of the output voltage signal level deviating from the bias level scaled to 0 and below the output signal level values scaled to +1 and −1. Other scales may be employed, depending on the signal polarities and ranges employed. The sensor 50 may include its own microprocessor with autocalibration of offset error and drift (possibly caused by temperature variation or other things).

	TABLE 2
	Posture	ax	ay	az
	UP	0	+1	0
	SUPINE	0	0	+1
	PRONE	0	0	−1
	RIGHT	−1	0	0
	LEFT	+1	0	0

Table 2 sets forth the ideal, scaled amplitudes of the output signals, ax, ay, and az, respectively, of a three-axis accelerometer employed in sensor 50 and incorporating into implantable device 10. (The units in the ideal example would be in gravity or “g”). One axis of the accelerometer (a_y) is aligned to earth's gravitational field when the implantable device 10 is implanted. Thus, when standing upright and remaining still, the amplitude or level of the output signal a_y of three-axis accelerometer should be at +1. In this orientation, the scaled amplitudes of the output signals az and ax of the three-axis accelerometer, respectively, should approach 0.

The scaled amplitude of the output signal az of the three-axis accelerometer should approach +1 or −1, respectively, when the patient lies still and is either supine or prone on their back or stomach and if the INS 10 is implanted with the z-axis of the three axis accelerometer aligned in a posterior-anterior position. In these positions, the amplitudes of the output signals a_y and ax of the three-axis accelerometer, respectively, should approach 0. In the same fashion, the patient lying on the right and left sides will orient the sensitive axis of the three-axis accelerometer with earth's gravitational field to develop the scaled amplitude of either −1 or +1 of the output signal a_x. The amplitudes of the output signals a_y and a_z of the three-axis accelerometer should approach 0. In these ideal orientations of Table 2, there is no rotation of the axes of the INS 10 with respect to earth's gravitational field.

As will be appreciated, the determination described above identifies the pose of the implantable device 10 and not necessarily the patient in which it is implanted. In practice the implantable device 10 will rarely if ever be implanted in the patient such that the three axes of the three-axis accelerometer precisely align the idea orientations of Table 1. Accordingly, following implantation of the implantable device 10, a series of calibration tests can be undertaken during which the patient is alternated from standing to lying, from prone to supine, and from right to left sides. By acquiring a series of such values, the sensor 50 can be calibrated for the implantation, to determine the voltage output values of each of the three axes of the accelerometer in each of the positions. Further, though not described in detail herein, similar analyses may be undertaken to determine when a person is in a slightly reclined position such as when sitting in an airplane seat or other position.

In another sensor 50, a three-axis accelerometer acts, as noted above, as a motion detector. This motion detector is tuned (e.g., using one or more band pass filters) to detect lung vibrations in the patient caused by respiration.

The sensor 50 may be an ECG sensor. ECG is a recording of the electrical activity of the heart over a period of time. While an ECG typically employs sensors placed on the skin, an effective ECG can be employed in an implantable device wherein at least two electrodes separated by a distance (e.g., at least about 35 mm) are employed to detect electrical changes caused by the cardiac depolarization and repolarization during each cardiac cycle.

Yet a further sensor 50 that may be employed is an EEG system from which the sleep stages of the patient may be determined. The EEG may include sensors implanted in the patient and operably connected to the implantable device 10. Alternatively, the sensors may be implanted in the patient and operably connected to a remove or satellite implanted device located above the shoulders of the patient and in communication with the implantable device 10. Still further the sensors may be a wearable set of sensors that are in communication with the implantable device.

In view of inclusion of one or more of these sensors 50, a corollary set of data can be constructed to that from a sleep study. For example, the total sleep time (TST) can be derived by comparing the time period that the patient (an implantable device 10) is in a lying down position, either prone or supine, and the time where the motion sensor detects motion consistent with a sleeping heart rate, or with motion consistent with sleeping. Once a TST is determined, a sleep efficiency can also be derived by comparing the TST to the total recording time (TRT) which may be the entirety of the period that the patient is in the lying down position.

Sleep stages, as in the case of a formal sleep study might require the use of EEG data from the EEG sensors, however, arousals or awakenings can be derived from the posture sensor. These would be instances where the patient transitioned from one to another posture and depending on the period of time between the beginning of the transition the transition can be characterized as an arousal or awakening. Gross motion data from a motion sensor, consistent with for example walking to the bathroom, or other data can also be overlaid on the data from the posture detector to further assist in classifying the detected movements or change in posture as an awakening or an arousal.

Respiration rate may be derived by a number of methods. As noted above, a three-axis accelerometer may be tuned to the vibrations of the lungs. By such tuning the change in position of the sensor 50 can be plotted and normalized to provide a respiration rate for the patient. Further ECG data, as might be acquired from ECG sensors is known to be proportional to respiration rate. In this way as the ECG baseline shifts, as a result of increased heart rate, a proportion change in respiration rate can be determined. Similarly, an optical sensor, such as the reflectance blood-oxygen saturation sensor described above to measure blood-oxygen saturation levels may also be employed to determine a pulse transit time. A shift in this transit time is also known to be proportional with a chance in respiration rate. For both the ECG baseline and optical sensor baseline shifts, a normal range of both of these values for the patient while sleeping may be required to determine these changes in respiration rate.

With respect to the respiration rate, any or all of these respiration rates may be employed to develop an AHI value. By comparing changes in the lung vibration, and changes in the baseline of the ECG and pulse transit times, an initial approximation of instances of an apnea can be identified. When any of these occur, the blood-oxygen saturation level sensor can be triggered to record the blood-oxygen saturation level for a given period of time following the event (assuming it is not being constantly monitored). Where a change in respiration rate is observed, if it is followed by a drop in blood-oxygen saturation level, it can reasonably be identified as an apnea, as described above with respect to Table 1. As those are measured on any given night's sleep and over the course of days, weeks, and years the development of and the incidence of sleep apnea can be assessed and actively monitored by health care providers in coordination with the treatment of the co-morbid heart conditions.

Though described herein largely in the context of sensors 50 that form part of the implantable device 10, this instant disclosure is not so limited. As noted elsewhere one or more of the sensors including the EEG sensors, the leg movement sensors, the ECG sensor, the blood-oxygen saturation level sensor, and others may be external sensors 100 (FIG. 2) formed external to the patient and the implantable device without departing from the scope of the disclosure. These external sensors 100 may be in communication the external programmer 90 or directly with a remote server (e.g., a cloud-based data system).

A further aspect of the disclosure is described in connection with FIGS. 2 and 3 in which a simplified diagram of a system 200 is depicted, and a method of the systems operation are described. The system 200 includes an implantable device 10, an external programmer 90, one or more external sensors 100, a remote server 202 in communication with the external programmer 90, external sensors 100, and a remote computer 204. Those of ordinary skill in the art will appreciate that the remote computer 204 may be an external programmer 90, particularly one configured for use by a health care provider.

Following implantation of the implantable device 10, (step 700) the data collected from sensor 50 is downloaded to the external programmer 90 (step 710). This data from the sensor 50 may be combined with various self-reported data that a user may input via a user interface on the external programmer 90 (step 720). In one embodiment of the disclosure the external programmer 90, or another device (not shown) in communication with the server 202, presents the patient with a user interface. The user interface may be presented to the user on a periodic basis including daily, weekly, bi-weekly, or monthly. In accordance, with the daily embodiment, the user interface may request that the patient input various self-reporting data. Data from external sensors 100 or other appliances may also be reported either to the external programmer 90 or directly to the remote server 202.

As noted above, the sensor 50 can provide a variety of data dependent upon how it is configured. The sensor 50 can be a motion sensor, heart rate detector, ECG sensor, EEG sensor, posture detector, blood-oxygen saturation detector, respiration rate detector, leg movement sensor, and others. These sensors may be formed of various sub-components including, but not limited to accelerometers tuned to detect specific types movement and vibrations as disclosed elsewhere herein.

As one example using the posture detection data, either alone or in combination with heart rate or respiration rate, a sleep start and end time may be determined. Using one or more accelerometers and a variety of bandpass filtering position, activity (arousals vs awakenings), sleep stages, respiration rate, and heart rate can be collected. This data can be reported to the control circuit 20 and stored in memory 30 at least temporarily. The external programmer 90 can be set to automatically interface with the implantable 10 every day, every hour, or at another periodic or scheduled interval. The external programmer 90 downloads the sensor data via the telemetry circuit 60 (step 710) and receives the external sensor data and self-reported data at step 720. The external programmer may optionally communicate the sensor data from the implantable device and any and self-reported data entered via the user-interface to the server 202 (step 725).

Either the server 202 or the external programmer 90 may include thereon one or more software applications. One of these applications may review the data received from the external programmer 90 and assess whether the patient having the implantable device 10 shows indications of suffering from sleep apnea. For example, a patient who registers a low sleep efficiency (TST/TRT) value, a relatively high number of arousals or awakenings, an AHI value of greater than 15, and drops in blood-oxygen saturation levels following each occurrence of an apnea would provide strong indication that the patient suffers from at least moderate sleep apnea. The application running on the external programmer 90 or server 202 may analyze these and other data at step 730 and report an assessment to a health care provider via remote computer 204 at step 740 or directly to the patient via the user interface on the external programmer 90 at steps 750. This collection of data and determining of a sleep score (steps 710-730) may be iterative repeated prior to advancing to the next step.

Either the healthcare provider, accessing the remote computer 204, can receive the assessment from the server 202 via the remote computer. This may be as part of assessing other data related to the patient. For example, where the implantable device 10 is pacemaker, the health care provider may periodically analyze the heart rhythms and interventional actions of the pacemaker. On a user interface presented to the health care provider via the remote computer 204, in addition to the standard heart related data related directly to the implantable device 10, an alert or other indication may be presented to the health care provider indicating that the application has determined that the patient may suffer from to common comorbidity of sleep apnea.

The remote computer 204 also provides access to the raw data and computed data derived from the data collected by the sensor 50 and from external sensors 100 (described above). Thus, the health care provider can analyze the collected and computed data in much the same manner as a health care provider might analyze the data collected during a traditional sleep study, as described above.

Whether relying on the indication provide by the application running on the server 202 or based on the health care provider's own assessment of the data the health care provider can initiate communications with the patient. As will be appreciated the communication can range from relying solely on the data collected via the sensor 50 of the implantable device 10 and external sensors 100 to start a treatment regimen for sleep apnea to scheduling a formal sleep study.

Similarly, the application running on the external programmer 90 can present one or more user interfaces to the patient where an initial assessment of the patient's likelihood of suffering from sleep apnea can be indicated. This may include an indication of the sleep score the patient received for the prior night's sleep, historical comparison of their sleep score and even access to some or all of the raw data from which the sleep score is derived. Further, the user interface may present a suggestion to contact their health care provider, an opportunity to make an appointment with their health care provider, or even access to emergency services if warranted. In addition, even in instances where the sleep score and other determinations are made on the external programmer 90, the external programmer may nonetheless be in communication with the server 202 to enable further processing and storage of the data the external programmer 90 has analyzed.

In a further aspect of the disclosure, the server 202 may collect or be in communication one or more further servers receiving similar data from other patients. The entirety of the collected data may then be analyzed by one or more neural networks to assess the combined data and to identify patterns within the data to provide indications to health care providers related to both an individual patient that may require treatment and therapy, and to provide a global assessment of a larger population of patients. Some of these patients will have similar comorbidities, and others will not. By further assessment of the data the neural network can seek out similar groups of patients and provide information to health care providers regarding the likelihood of sleep apnea even before implantation of the implantable device based on these similarities (e.g., age, demographics, weight, heart disease, blood pressure, etc.). Further, the data from the implantable devices can be constantly assessed by the neural networks to assess the population of patients having implantable devices to diagnose sleep apnea co-morbidities. Additionally or alternatively, the server 202 may include one or more applications employing fuzzy logic to analyze the data from both an individual and from the broader community of patients (step 730).

As a further aspect of the present disclosure, prior to implantation of an implantable device 10, the patient may have already undergone a patient assessment of sleep apnea with their health care provider. During this assessment a variety of self-reported issues may be identified including daytime sleepiness, interrupted snoring, gasping, co-morbidities, etc. The data related to these issues may be stored on the server 202 as part of the patient electronic medical records (EMR), these data may also be analyzed as part of the application's assessment of the data received from sensor 90. Further, the EMR may include the results of a prior sleep study undertaken by the patient. These data may be recorded by a remote computer 204, either directly or via additional hardware, and saved to the remote server 202.

As will be appreciated, sleep apnea is a degenerative disease in that it manifests itself and worsens over time. Thus, even if an in initial sleep study is inconclusive, or does not result in treatment for sleep apnea, over time the patient's condition may worsen and require treatment as a result of or in conjunction with the worsening of their co-morbid conditions.

Though generally having been described herein as having been undertaken at the server 202, any of the calculations and analyses of the sensor data from sensor 50 and described herein can be undertaken by the control circuit 20 or by an application running on the external programmer 90. In this manner the results of these calculations, along with any of the direct sensor data may be displayed to a patient or a health care provider on the external programmer 90 as part of a user interface displayable therein. This may include an indication to the patient that they display symptoms of suffering from sleep apnea and should seek attention from their health care provider. Similarly, even if the calculations and analyses are performed by one or more applications running on the server 202, because the server 202 is in communication with external programmer 90 the results of those calculations and analyses may be transmitted back to the external programmer 90 and presented to the patient on a user interface displayable thereon. Again, this may include an indication to the patient that they appear to display symptoms of sleep apnea and should seek attention from their health care provider.

It should be understood that, depending on the example, certain acts or events of any of the methods described herein can be performed in a different sequence, may be added, merged, or left out altogether (e.g., not all described acts or events are necessary for the practice of the method). Moreover, in certain examples, acts or events may be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors, rather than sequentially. In addition, while certain aspects of this disclosure are described as being performed by a single module or unit for purposes of clarity, it should be understood that the techniques of this disclosure may be performed by a combination of units or modules associated with, for example, a medical device.

In one or more examples, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include computer-readable storage media, which corresponds to a tangible medium such as data storage media (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).

Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor,” as used herein may refer to any of the foregoing structure or any other structure suitable for implementation of the techniques described herein. Also, the techniques could be fully implemented in one or more circuits or logic elements.

Thus, an implantable medical device system has been presented in the foregoing description with reference to specific examples. It is to be understood that various aspects disclosed herein may be combined in different combinations than the specific combinations presented in the accompanying drawings. It is appreciated that various modifications to the referenced examples may be made without departing from the scope of the disclosure and the following claims.

Claims

1. A method of assessing a patient for sleep apnea comprising:

receiving sensor data from a device implanted in a patient for treatment of a heart related disease at an external programmer;

transmitting the received sensor data to a remote server;

analyzing the received data at the server; and

transmitting to a remote computer an assessment of the received sensor data, wherein the assessment includes an evaluation of sleep apnea for the patient.

2. The method of claim 1, further comprising receiving sensor data from an external sensor.

3. The method of claim 1, further comprising receiving self-reported data.

4. The method of claim 1, wherein the sensor data includes data indicative of the posture of the patient, motion data of the patient, electroencephalogram (EEG) data, electrocardiogram (ECG) data, Apnea Hypopnea Index (AHI) data or blood-oxygen saturation data.

5. The method of claim 1, wherein the sensor data includes data from one or more three-axis accelerometers.

6. The method of claim 1, wherein the sensor data includes respiration rate data, heart rate data, total sleep time data, sleep efficiency data, sleep stage data, arousals data, or awakenings data.

7. The method of claim 1, wherein the implanted device selected from the group consisting of a pacemaker, an implantable cardiac defibrillators (ICD), a cardiac resynchronization therapy (CRT) device, and an implantable neurostimulator (INS).

8. The method of claim 1, further comprising a neural network performing the analysis of the received data.

9. A system comprising:

an implantable device including a sensor, a telemetry circuit and a memory;

an external programmer in communication with the telemetry circuit and configured to receive data collected by the sensor and stored in the memory;

a server, including a processor, in communication with the external programmer and storing thereon an application including instructions that when executed by the processor executes steps of: receiving the data collected by the sensor from the external programmer; and analyzing the received data collected by the sensor; and transmitting to a remote computer an assessment of the received sensor data, wherein the assessment includes an evaluation of sleep apnea for patient in which the implantable device has been implanted.

10. The system of claim 8, further comprising one or more external sensors configured to transmit sensor data to the server or the external programmer.

11. The system of claim 8, further comprising a user-interface presented on the external programmer and configured to receive self-reported data.

12. The system of claim 8, wherein the sensor outputs data indicative of the posture of the patient, motion of the patient, an electroencephalogram (EEG), an electrocardiogram (ECG), an Apnea Hypopnea Index (AHI) or blood-oxygen saturation.

13. The system of claim 8, wherein the sensor is comprised of one or more three-axis accelerometers.

14. The system of claim 8, wherein the sensor outputs data indicative of respiration rate, heart rate, total sleep time, sleep efficiency, sleep stage, arousals, or awakenings.

15. The system of claim 8, wherein the implanted device selected from the group consisting of a pacemaker, an implantable cardiac defibrillators (ICD), a cardiac resynchronization therapy (CRT) device, and an implantable neurostimulator (INS).

16. The system of claim 8, further comprising a neural network performing the analysis of the received data at the server.

17. A computer readable recording medium storing thereon instructions that when executed by a processor and cause the processor to execute the steps of:

receiving sensor data from an external processor, the sensor data having been collected by an implanted device configured for treatment of a heart related disease;

analyzing the received sensor data; and

transmitting to a remote computer an assessment of the received sensor data, wherein the assessment includes an evaluation of sleep apnea for a patient in which the implantable device has been implanted.

18. The computer readable recording medium of claim 17, wherein the sensor data includes data indicative of the posture of the patient, motion data of the patient, electroencephalogram (EEG) data, electrocardiogram (ECG) data, Apnea Hypopnea Index (AHI) data or blood-oxygen saturation data.

19. The computer readable recording medium of claim 17, wherein the sensor data includes data from one or more three-axis accelerometers.

20. The computer readable recording medium of claim 17, wherein the sensor data includes respiration rate data, heart rate data, total sleep time data, sleep efficiency data, sleep stage data, arousals data, or awakenings data.