# VARIABLE INDICATION ESTIMATOR

A variable indication estimator which determines an output value representative of a set of input data. For example, the estimator can reduce input data to estimates of a desired signal, select a time, and determine an output value from the estimates and the time. In one embodiment, the time is selected using one or more adjustable signal confidence parameters determine where along the estimates the output value will be computed. By varying the parameters, the characteristics of the output value are variable. For example, when input signal confidence is low, the parameters are adjusted so that the output value is a smoothed representation of the input signal. When input signal confidence is high, the parameters are adjusted so that the output value has a faster and more accurate response to the input signal.

**Description**

**INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS**

Any and all applications, if any, for which a foreign or domestic priority claim can be identified in the Application Data Sheet of the present application is hereby incorporated by reference under 37 CFR 1.57.

**FIELD OF THE INVENTION**

The present invention is directed to the field of signal processing, and, more particularly, is directed to systems and methods for determining a representative estimate output value for a window of input data.

**BACKGROUND OF THE INVENTION**

Digital signal processing techniques are frequently employed to enhance a desired signal in a wide variety of applications, such as health care, communications and avionics, to name a few. Signal enhancement includes smoothing, filtering and prediction. These processing techniques each operate on a block of input signal values, such as, for example, a window of input signal values, in order to estimate the signal at a specific point in time. **100** and a block **101** of input signal values depicted in this example as occurring within a time window between points t^{min }and t^{max}. Specifically, the block **101** includes a set of discrete input values {v_{i}; i=1, 2, . . . n} occurring at a corresponding set of time points {t_{i}; i=1, 2, . . . n}. A smoother operates on the block **101** of input values to estimate the signal at a time point, t_{s }**102** between t^{min }and t^{max}. That is, a smoother generates an output value based upon input values occurring before and after the output value. A filter operates on the block **101** of input values to estimate the signal at a time t_{f }**104**, corresponding to the most recently occurring input value in the block **101**. That is, a filter generates a forward filtered output value at the time t_{f }based upon input values occurring at, and immediately before, the output value. A filter also operates on the block **101** to estimate the signal at a time t_{b }**105** at the beginning of the block **101** to generate a backward filtered value. A forward predictor operates on the block of input values **101** to estimate the signal at time t_{pf }**106**, which is beyond the most recently occurring value in the block **101**. That is, a forward predictor generates a forward predicted output value based upon input values occurring prior to the output value. A backward predictor operates on the block **101** of input values to estimate the signal at time t_{pb }**108**, which is before the earliest occurring value in the block **101**. That is, a backward predictor generates a backward predicted output value based upon input values occurring after the output value.

**SUMMARY OF THE INVENTION**

A common smoothing technique uses an average to fit a constant, v^{A}, to a set of data values, {v_{i}; i=1, 2, . . . , n}:

A generalized form of equation (1) is the weighted average

Here, each value, v_{i}, is scaled by a weight, w_{i}, before averaging. This allows data values to be emphasized and de-emphasized relative to each other. If the data relates to an input signal, for example, values occurring during periods of low signal confidence can be given a lower weight and values occurring during periods of high signal confidence can be given a higher weight.

_{i}; i an integer} **110**. The input signal **110** may be, for example, a desired signal corrupted by noise or a signal having superfluous features. The constant mode averager suppresses the noise and unwanted features, as described with respect to **132** defines a first set, {v_{i}; i=1, 2, . . . , n}, of signal values, which are averaged together to produce a first output value, z_{1 }**122**. A second time-window **134**, shifted from the previous window **132**, defines a second set {v_{i}; i=2, 3, . . ., n+1}of signal values, which are also averaged together to produce a second output value z_{2 }**124**. In this manner, a discrete output signal, {z_{j}; j an integer} **120** is generated from a moving weighted average of a discrete input signal {v_{i}; i an integer} **110**, where:

A common filtering technique computes a linear fit to a set of data values, {v_{i}; i=1, 2, . . . , n}:

*{circumflex over (v)}*_{i}*=α·t*_{i}+β (4)

where α and β are constants and t_{i }is the time of occurrence of the i^{th }value. _{i}; i an integer} **110**. The input signal **110** may be, for example, a desired signal with important features corrupted by noise. The linear mode averager reduces the noise but tracks the important features, as described with respect to **132** defines a first set, {v_{i}; i=1, 2, . . . , n}, of signal values. A linear fit to these n values is a first line **240**, and the value along this line at max{t_{1}, t_{2}, . . . , t_{n}} is equal to a first output value, z_{1 }**222**. A second time-window **134** shifted from the previous window **132** defines a second set, {v_{i}; i=2, 3, . . . , n+1}, of signal values. A linear fit to these n values is a second line **250**, and the value along this line at max{t_{2}, t_{3}, . . . , t_{n+1}} is equal to a second output value, z_{2 }**224**. In this manner, a discrete output signal, {z_{j}; j an integer} **220** is generated from a moving linear fit of a discrete input signal {v_{i}; i an integer}, where:

In general, the time windows shown in _{i}'s may not be in increasing or decreasing order or uniformly distributed, and successive time windows may be of different sizes. Also, although the discussion herein refers to signal values as the dependent variable and to time as the independent variable to facilitate disclosure of the present invention, the concepts involved are equally applicable where the variables are other than signal values and time. For example, an independent variable could be a spatial dimension and a dependent variable could be an image value.

The linear mode averager described with respect to

Conventionally, the least-mean-squared (LMS) error is calculated by setting the partial derivatives of equation (6b) with respect to α and β to zero:

Substituting equation (6b) into equation (7b) and taking the derivative yields:

Solving equation (8) for β and substituting the expression of equation (2) yields:

where the weighted average time, t^{WA}, is defined as:

Substituting equation (9b) into equation (4) gives:

*{circumflex over (v)}*_{i}=α(*t*_{i}*−t*^{WA})+*v*^{WA } (11)

Substituting equation (11) into equation (6a) and rearranging terms results in:

Changing variables in equation (12) gives:

where:

*v′*_{i}*=v*_{i}*−v*^{WA } (14a)

*t′*_{i}*=t*_{i}*−t*^{WA } (14b)

Substituting equation (13) into equation (7a) and taking the derivative yields

Solving equation (15) for a gives:

Substituting equations (14a, b) into equation (16) results in:

where:

Finally, substituting equation (17b) into equation (11) provides the equation for the least-mean-square (LMS) linear fit to {v_{i}, i=1, 2, . . . , n}:

_{i}=1, 2, . . . , n} **310**. The constant mode averager calculates a constant **320** for these values **310**, which is equal to v^{WA}, the weighted average of the input values v_{i}. Thus, the constant mode averager output **340** has a value V^{WA}. For comparison to the linear mode averager, the constant mode averager output can be conceptualized as an estimate of the input values **310** along a linear fit **350**, evaluated at time t^{WA}. The linear mode averager may be thought of as calculating a LMS linear fit, {circumflex over (v)}_{i }**330** to the input signal values, v_{i }**310**. The linear mode averager output **350** has a value, The linear mode averager output is an estimate of the input values **310** along the linear fit **330**, described by equation (19), evaluated at an index i such that t_{i}=t^{MAX}.

where:

*t*^{MAX}*=max {t*_{1}*, t*_{2}*, . . . , t*_{n}} (21)

As illustrated by **360** between the constant mode averager output value **340** and the linear mode averager output value **350**.

**410**, which increases in frequency with time. **400**. **500** in response to the input signal **410**, with the noise-free signal **400** shown for reference. **600** in response to the input signal **410**, with the noise-free signal **400** also shown for reference. As shown in **500** suppresses noise from the input signal **410** (**400** as frequency increases. As shown in **600** tends to track the input signal **400** but also tracks a portion of the noise on the input signal **410**.

One aspect of the present invention is a variable mode averager having a buffer that stores weighted input values. A mode input specifies a time value relative to the input values. A processor is coupled to the buffer, and the processor is configured to provide an estimate of the input values that corresponds to the time value. In a particular embodiment, the mode input is adjustable so that the estimate varies between that of a smoother and that of a forward predictor of the input values. In another embodiment, the mode input is adjustable so that the estimate varies between that of a smoother and that of a filter of the input values. In yet another embodiment, the mode input is adjustable so that the estimate varies between that of an average of the input values and that of a filter of the input values. The mode input may be adjustable based upon a characteristic associated with the input values, such as a confidence level. In one variation of that embodiment, the estimate can be that of a smoother when the confidence level is low and that of a filter when the confidence level is high. The estimate may occur along a curve-fit of the input values at the time value. In one embodiment, the curve-fit is a linear LMS fit to the input values.

Another aspect of the present invention is a signal averaging method. The method includes identifying signal values and determining weights corresponding to the signal values. The method also includes computing a trend of the signal values adjusted by the weights. Further, the method includes specifying a time value relative to the signal values based upon a characteristic associated with the signal values and estimating the signal values based upon the trend evaluated at the time value. The method may also incorporate the steps of determining a confidence level associated with the signal values and specifying the time value based upon the confidence level. In one embodiment, the trend is a linear LMS fit to the signal values adjusted by the weights. In that case, the time value may generally correspond to the maximum time of the signal values when the confidence level is high and generally correspond to the weighted average time of the signal values when the confidence level is low.

Yet another aspect of the present invention is a signal averaging method having the steps of providing an input signal, setting a mode between a first mode value and a second mode value and generating an output signal from an estimate of the input signal as a function of said mode. The output signal generally smoothes the input signal when the mode is proximate the first mode value, and the output signal generally tracks the input signal when the mode is proximate the second mode value. The method may also include determining a characteristic of the input signal, where the setting step is a function of the characteristic. In one embodiment, the characteristic is a confidence level relating to the input signal. In another embodiment, the setting step incorporates the substeps of setting the mode proximate the first mode value when the confidence level is low and setting the mode proximate the second mode value when the confidence level is high. In another embodiment, the input signal is a physiological measurement and the setting step comprises setting the mode proximate the first mode value when the measurement is corrupted with noise or signal artifacts and otherwise setting the mode proximate the second mode value so that the output signal has a fast response to physiological events.

A further aspect of the present invention is a signal averager having an input means for storing signal values, an adjustment means for modifying the signal values with corresponding weights, a curve fitting means for determining a trend of the signal values, and an estimate means for generating an output value along the trend. The signal averager may further have a mode means coupled to the estimate means for variably determining a time value at which to generate the output value.

For purposes of summarizing the invention, certain aspects, advantages and novel features of the invention have been described herein. Of course, it is to be understood that not necessarily all such aspects, advantages or features will be embodied in any particular embodiment of the invention.

**BRIEF DESCRIPTION OF THE DRAWINGS**

A general architecture that implements the various features of the invention will now be described with reference to the drawings. The drawings and the associated descriptions are provided to illustrate embodiments of the invention and not to limit the scope of the invention. Throughout the drawings, reference numbers are re-used to indicate correspondence between referenced elements. In addition, the first digit of each reference number indicates the figure in which the element first appears.

**DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS**

Equation (22) is a modified form of equation (20), which is motivated by equations (2) and (19) along with recognition of the relationships in Table 1.

^{WA}

^{WLA}

^{MWLA}

As shown in Table 1, the Variable Mode Averager in accordance with the present invention includes the constant mode averager processing function and the linear mode averager processing function, which are known processing functions. As further shown in Table **1**, the Variable Mode Averager of the present invention also includes a variable mode averager processing function, which will be described below.

As shown in Table 1, if mode=0, the variable mode averager output is v^{WA}, the output of the constant mode averager function, which utilizes a weighted average of the input signal values. If mode=1, the variable mode averager output is V^{WLA}, the output of the linear mode averager function, which utilizes a LMS linear fit to the input signal values. If 0<mode<1, then the variable mode averager output is V^{MWLA }and has output characteristics that are between that of the constant mode averager and the linear mode averager. In addition, if mode>1, then the variable mode averager behaves as a forward predictor.

As shown in **720** is an estimate of the input values at a selected time along the linear fit **710**, which indicates a trend of the input values. Assuming 0<mode<1, the mode variable determines the equivalent time **730** between t^{WA }and t^{MAX }for which the estimate is evaluated, yielding an output value **740** between V^{WA }and V^{WLA}. Thus, the mode variable acts to parametrically vary the time delay between the input and output signals of the variable mode averager, along with associated output characteristics. If mode=0, the time delay **360** (

The variable mode averager has been described in terms of weighted input values. One of ordinary skill, however, will recognize that the present invention includes the case where all of the weights are the same, i.e., where the input values are equally weighted or unweighted. Further, although the variable mode averager has been described in terms of a linear mode averager, one of ordinary skill in the art will recognize that a variable mode averager could also be based on non-linear curve fits, such as exponential or quadratic curves indicating a non-linear trend of the input signal. In addition, one of ordinary skill will understand that the variable mode averager can be implemented to operate on continuous data as well as infinitely long data. Also, a variable mode averager based upon a linear fit by some criteria other than LMS; a variable mode averager using any mode value, including negative values; and a variable mode averager based upon a linear fit where t^{min}=min {t_{1}, t_{2}, . . . t_{n}} is substituted for t^{MAX }in equation (22) are all contemplated as within the scope of the present invention.

**800** of a variable mode signal averager. After an entry point **802**, variables are initialized to zero in a block **808**. Next, in a block **812**, the sums of various parameters are calculated by summing the products of corresponding values in each of three buffers: an input data buffer, value[i]; a weight buffer, weight[i]; and a time value buffer, time[i]. In addition, the weight[i] values are summed. These sums are calculated over the entire length of each buffer, representing a single time window of n values. The calculations are performed by incrementing a loop counter i in a block **810** and reentering the block **812**. The loop counter i specifies a particular value in each buffer. Each time through the block **812**, the variable mode signal averager generates products of buffer values and adds the results to partial sums. After completing the partial sums, the variable mode signal averager then determines if the ends of the buffers have been reached in a decision block **814** by comparing the incremented value of i to the size of the buffer. If the ends of the buffers have not been reached, the variable mode averager increments the loop counter i and reenters the block **812**; otherwise, the variable mode averager continues to a decision block **816**.

In the decision block **816**, a check is made whether the sum of the weights, sumw, is greater than zero. If so, each of the sums of the products from the block **812** is divided by sumw in a block **820**. In the block **820**, the parameters computed are:

sumwv, the weighted average value of equation (2);

sumwt, the weighted average time of equation (10);

sumwvt, the weighted average product of value and time; and

sumwt2, the weighted average product of time squared.

The sumwt2 parameter from the block **820** is then used in a block **822** to calculate an autovariance sigma2tt in accordance with equation (18b). If, in a decision block **824**, a determination is made that the autovariance is not greater than zero, then in a decision block **825**, a determination is made whether the sum of the weights is greater than zero. If, in the decision block **825**, the sum of the weights is not greater than zero, then an output value, out, which was initialized to zero in the block **808**, is returned as a zero value at a termination point **804**. Otherwise, if, in the decision block **825**, a determination is made that the sum of the weights is greater than zero, then in a block **826**, the value of the sum of the weights is assigned to the output value, out, and the output value is then returned at the termination point **804**.

If, in the decision block **824**, the autovariance is determined to be greater than zero, then in a block **827**, the sumwvt parameter from the block **820** is used to calculate a crossvariance signal sigma2vt in accordance with equation (18a). Thereafter, the maximum time, t^{MAX}, as defined in equation (21), is determined by finding the largest time value in the time buffer, time[i]. In particular, in a block **829**, the loop counter, i, is reinitialized to zero and the value of t^{MAX }is initialized to zero. Next, in a decision block **832**, the current value of t^{MAX }is compared to the current value of the time buffer indexed by the loop counter, i. If the current value of t^{MAX }is not less than the current value of the time buffer or if the current weight value indexed by i is not greater than zero, then t^{MAX }is not changed and a block **834** is bypassed. On the other hand, if the current value of t^{MAX }is less than the current time value and if the current weight value is greater than zero, then the block **834** is entered, and the value of t^{MAX }is replaced with the current time value time[i]. In either case, in a decision block **838**, the loop counter, i, is compared to the buffer size, and, if the loop counter, i, is less than the buffer size, the loop counter, i, is incremented in a block **830**, and the comparisons are again made in the decision block **832**.

When, in the decision block **838**, it is determined that the loop counter, i, has reached the buffer size, the variable mode averager proceeds to a block **840** with the largest value of time[i] saved as the value of t^{MAX}. In the block **840**, a single output value, out, is computed in accordance with equation (22). Thereafter, the output value, out, is limited to the range of values in the input data buffer, value[i]. This is accomplished by comparing out to the maximum and minimum values in the data buffer. First, in a block **850**, the maximum of the value buffer is determined. Then, in a decision block **852**, the maximum of the value buffer is compared to out. If out is bigger than the maximum of the value buffer, then, in a block **854**, out is limited to the maximum value in the buffer. Otherwise, the block **854** is bypassed, and out remains as previously calculated in the block **840**. Thereafter, in a block **860**, the minimum of the value buffer is determined. The minimum of the value buffer is compared to out in a decision block **862**. If out is smaller than the minimum of the value buffer, then, in a block **864**, out is set to the minimum value in the buffer. Otherwise, the block **864** is bypassed, and out is not changed. The value of out determined by the block **840**, the block **852** or the block **864** is then returned from the routine via the termination point **804**.

In one embodiment, the process described with respect to

Pulse oximetry is one application that can effectively use signal processing techniques to provide caregivers with improved physiological measurements. Pulse oximetry is a widely accepted noninvasive procedure for measuring the oxygen saturation level of arterial blood, an indicator of oxygen supply. Early detection of low blood oxygen is critical in the medical field, for example in critical care and surgical applications, because an insufficient supply of oxygen can result in brain damage and death in a matter of minutes. Pulse oximeter systems are described in detail in U.S. Pat. No. 5,632,272, U.S. Pat. No. 5,769,785, and U.S. Pat. No. 6,002,952, which are assigned to the assignee of the present invention and which are incorporated by reference herein.

**900** utilizing a variable mode averager **960**. A pulse oximetry system **900** consists of a sensor **902** attached to a patient and a monitor **904** that outputs desired parameters **982** to a display **980**, including blood oxygen saturation, heart rate and a plethysmographic waveform. Conventionally, a pulse oximetry sensor **902** has both red (RED) and infrared (IR) light-emitting diode (LED) emitters (not shown) and a photodiode detector (not shown). The sensor **902** is typically attached to a patient's finger or toe, or to a very young patient's foot. For a finger, the sensor **902** is configured so that the emitters project light through the fingernail and into the blood vessels and capillaries underneath. The photodiode is positioned at the fingertip opposite the fingernail so as to detect the LED transmitted light as it emerges from the finger tissues, producing a sensor output **922** that indicates arterial blood absorption of the red and infrared LED wavelengths.

As shown in **922** is coupled to analog signal conditioning and an analog-to-digital conversion (ADC) circuit **920**. The signal conditioning filters and amplifies the analog sensor output **922**, and the ADC provides discrete signal values to the digital signal processor **950**. The signal processor **950** provides a gain control **952** to amplifiers in the signal conditioning circuit **920**. The signal processor **950** also provides an emitter control **954** to a digital-to-analog conversion (DAC) circuit **930**. The DAC **930** provides control signals for the emitter current drivers **940**. The emitter drivers **940** couple to the red and infrared LEDs in the sensor **902**. In this manner, the signal processor **950** can alternately activate the sensor LED emitters and read the resulting output **922** generated by the photodiode detector.

The digital signal processor **950** determines oxygen saturation by computing the differential absorption by arterial blood of the red and infrared wavelengths emitted by the sensor **902**. Specifically, the ADC **920** provides the processor **950** with a digitized input **924** derived from the sensor output **922**. Based on this input **924**, the processor **950** calculates ratios of detected red and infrared intensities. Oxygen saturation values, v_{i}, are empirically determined based on the calculated red and infrared ratios. These values are an input signal **962** to the variable mode averager **960**. Each of the input values, v_{i}, are associated with weights, w_{i}, which form a second input **964** to the averager **960**. The individual weights, w_{i}, are indicative of the confidence in particular ones of the corresponding saturation values, v_{i}. A third input **974** sets the mode of the averager **960**. The variable mode averager **960** processes the values, v_{i}, weights, w_{i}, and mode as described above with respect to _{i}. The values z_{i }are the averager output **968**, from which is derived the saturation output **982** to the display **980**.

The mode signal may be generated by an external source (not shown) or it may be generated by another function within the digital signal processor. For example, mode may be generated from the confidence level of the input signal as illustrated in **972** to a mode control process **970**. The mode control process **970** maps the signal confidence input **972** to the mode input **974** of the variable mode averager **960**. When the signal confidence is low, the mode control **970** sets mode to a relatively small value. Depending on the application, mode may be set close to zero. When the signal confidence is high, the mode control **970** sets mode to a relatively large value. Some applications may prefer a mode of one for a high signal confidence, but this is not a requirement. When the signal confidence is neither high nor low, mode is set to an intermediate value (in some applications, mode may be set to a value between zero and one) empirically to achieve a reasonable tradeoff between a fast saturation output response and saturation accuracy.

The signal quality of pulse oximetry measurements is adversely affected by patients with low perfusion of blood, causing a relatively small detected signal, ambient noise, and artifacts caused by patient motion. The signal confidence input **972** is an indication of the useful range of the pulse oximetry algorithms used by the digital signal processor **950** as a function of signal quality. This useful range is extended by signal extraction techniques that reduce the effects of patient motion, as described in U.S. Pat. No. 5,632,272, U.S. Pat. No. No. 5,769,785, and U.S. Pat. No. 6,002,952, referenced above. Signal confidence is a function of how well the sensor signal matches pulse oximetry algorithm signal models. For example, the red and infrared signals should be highly correlated and the pulse shapes in the pulsatile red and infrared signals should conform to the shape of physiological pulses, as described in U.S. patent application Ser. No. 09/471,510 filed Dec. 23, 1999, entitled Plethysmograph Pulse Recognition Processor, which is assigned to the assignee of the present invention and which is incorporated by reference herein. As a particular example, signal confidence can be determined by measuring pulse rate and signal strength. If the measured signal strength is within an expected range for the measured pulse rate, then the confidence level will be high. On the other hand, if the measured signal strength is outside the expected range (e.g., too high for the measured pulse rate), then the confidence level will be low. Other measured or calculated parameters can be advantageously used to set the confidence level.

**1010** illustrates oxygen saturation versus time for input oxygen saturation values processed by a conventional weighted averager or, equivalently, by a variable mode averager **960** with mode≈0. A second output **1020** illustrates oxygen saturation versus time for the variable mode averager **960** with mode≈1. Each output **1010**, **1020** indicates exemplary desaturation events occurring around a first time **1030** and a second time **1040**. The desaturation events correspond to a patient experiencing a potentially critical oxygen supply shortage due to a myriad of possible physiological problems. With mode≈1, the variable mode averager responds to the onset of the desaturation events with less lag time **1050** than that of the conventional weighted average. Further, the variable mode averager responds to the full extent of the desaturations **1060** whereas the conventional weighted average does not. When signal confidence is low, the variable mode averager is adjusted to provide similar smoothing features to those of a conventional weighted average. When signal confidence is high, however, the variable mode averager is advantageously adjusted to respond faster and more accurately to a critical physiological event. The fast response advantage of the variable mode averager has other physiological measurement applications, such as blood-pressure monitoring and ECG.

The variable mode averager has been disclosed in detail in connection with various embodiments of the present invention. One of ordinary skill in the art will appreciate many variations and modifications within the scope of this invention.

Thus, the variable mode averager disclosed in the foregoing advantageously allows a signal processor the ability to reduce a window of input values of, for example, a noisy signal, to a linear fit of estimates of the desired signal, where a selected output value from the estimates corresponds at least in part to the selection of a time or mode. The mode can correspond, for example, to a degree of confidence that the most recently received input signal is an accurate representation of the desired signal. However, a skilled artisan will recognize from the disclosure herein that other mechanisms can be used to reduce a set of input values to one or more appropriate output values.

For example, **1100** of a signal processor, according to an embodiment of the invention. As shown in **1100** includes BLOCK **1110**, where the signal processor reduces a set or window of input values to one or more or a set of estimates such as the foregoing linear fit of the variable mode averager, or the like. The process **1100** then moves to BLOCK **1112**, where the processor selects a time based, for example, on an indication of confidence that the set of input values represents a desired signal. The process **1100** in BLOCK **1114** then determines the output value from the one or more, or set of estimates, which corresponds to the selected time.

As will be appreciated by an artisan from the disclosure herein, a wide variety of processes or mechanisms can be used to reduce a set or window of input data to a set of estimates. For example, the processor can execute the foregoing variable mode averager, or other more conventional signal processing techniques, such as, for example, simple averaging, weighted averaging, linear averaging, filtering, prediction, or the like to reduce the set of input data before selecting an appropriate time using the mode or signal confidence.

According to one embodiment, the processor can reduce input data through segmentation of a window of input values. For example, **1210**, including a window **1212** of input values. According to one embodiment, the input signal **1210** comprises, for example, a desired signal corrupted by noise or a signal having superfluous features. **1212** to the linear fit **1214** of estimates using the foregoing variable mode averager. As disclosed in the foregoing, when 0<mode<1, the mode variable determines the equivalent time along the linear fit of estimates for which an output estimate can be evaluated, thereby yielding an output value between v^{WA }and v^{WLA}.

However, **1212** of input values into a plurality of segments, e.g., Segments A**1**, A**2**, A**3**, and A**4**. A artisan will recognize from the disclosure herein that the use of four segments in

According to one embodiment, the signal processor then determines one or more or a set of estimates corresponding to each segment. For example, in a straightforward implementation, the signal processor may select simple weighted averages **1216**, **1218**, **1220**, **1222**, as estimates for each of the Segments A**1**, A**2**, A**3**, and A**4**, respectively, of the window **1212** of input values. However, an artisan will recognize from the disclosure herein that the estimates for each segment may range in complexity from simple selection of one or more of the input values, to more complex calculations, such as application of the foregoing variable mode averager or the like for the input values of each segment. Moreover, the artisan will recognize from the disclosure herein that the signal confidence indicator could be used to select one, some, or all of the input values corresponding to one, some, or all, of the segments for the generation of the estimate values.

Once the estimates for each segment are determined, the signal processor selects a time corresponding to a degree of confidence that the input values represent a desired signal. A signal confidence indicator representative of whether the more recently received input signal values are accurate representations of a desired signal can be derived from, for example, an analysis of the amount of noise in the signal, comparing the signal to expected patterns or templates, or the like. The analysis of noise can include a measurement of the entropy of the signal, adherence of the signal to predetermined mathematical models based on a priori information about the expected or desired signal, or the like.

In the example illustrated in **1224** where the estimates **1216**-**1222** are to be evaluated. According to an embodiment using a more straightforward reduction of the segments, such as, for example, the simple weighted averaging, the signal processor may interpolate between estimates, such as, output value **1228**. When more complex mechanisms are used to reduce the input data, determination of the output value **1228** may be directly calculated, such as, for example, calculation of the output value using the variable mode averager. A skilled artisan will also recognize from the disclosure herein that the output value **1228** may comprise an interpolation between more complex estimates, such as, for example, zero, first, second, etc. order interpolation.

Selection of the time **1224** allows the signal processor to slide the output value along, for example, the exemplary line **1214** or one of the segment estimates **1216**-**1222**, thereby providing an output value deemed likely to indicate the value of the desired signal for the most recent input value of the time window **1212**. For example, as disclosed in the foregoing, when the signal confidence indicator represents a higher confidence in the input values, the output value **1228** may slide toward the most recent input values, whereas the output value **1228** may side in the opposite direction during a time of lower signal confidence.

The signal processing techniques disclosed in the foregoing, which use a confidence measure to select an output value from a set of estimates of a window of input values, is particular applicable to the monitoring of critical physiological parameters in patient-care settings. When applied to pulse oximeter oxygen saturation measurements, the mode parameter can be varied in real-time to achieve a tradeoff between the suppression of false alarms and signal artifacts and the immediate detection of life threatening oxygen desaturation events. For example, during the monitoring of physiological parameters, it is often common for motion artifacts or other abnormalities to appear in the input value stream. Such abnormalities often decrease the confidence measure, or mode, being used by the signal processor. As disclosed in the foregoing, a lower signal confidence may lead to the signal processor selecting a smoothed output estimate for a specific time window, such as for example, time windows ranging from approximately 15 seconds to over 1 minute, thereby avoiding crossing over alarm-activating output thresholds. Alternatively, as discussed with reference to

Although the foregoing invention has been described in terms of certain preferred embodiments, other embodiments will be apparent to those of ordinary skill in the art from the disclosure herein. Additionally, other combinations, omissions, substitutions and modifications will be apparent to the skilled artisan in view of the disclosure herein. Accordingly, the present invention is not intended to be limited by the reaction of the preferred embodiments which disclose by way of example only, but is to be defined by reference to the appended claims.

Additionally, all publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.

## Claims

1-20. (canceled)

21. An apparatus for selecting output parameter values, the apparatus comprising:

- a processor configured to: receive an input physiological signal from a sensor coupled with a living being; calculate parameter values from the input physiological signal; select between a first parameter value from a trend of the parameter values and a second parameter value representing a constant average value of the parameter values within a time window, the trend having a non-zero slope in the time window; and output the selected parameter value.

22. The apparatus of claim 21, wherein the processor is further configured to select the first parameter value from the trend of the parameter values at a most recent time in the time window.

23. The apparatus of claim 21, wherein the processor is further configured to select the first parameter value from the trend of the parameter values at a time in the time window other than a most recent time.

24. The apparatus of claim 21, wherein the processor is further configured to select the first parameter value in the time window and the second parameter value in a second time window.

25. The apparatus of claim 21, wherein the processor is further configured to calculate a signal confidence using the input physiological signal.

26. The apparatus of claim 25, wherein the processor is further configured to make said selection using the signal confidence.

27. The apparatus of claim 26, wherein the processor is further configured to select the first parameter value when the signal confidence is high and the second parameter value when the signal confidence is low.

28. The apparatus of claim 21, wherein the parameter values comprise oxygen saturation values.

29. The apparatus of claim 21, wherein the parameter values comprises blood pressure values.

**Patent History**

**Publication number**: 20150351697

**Type:**Application

**Filed**: Aug 19, 2015

**Publication Date**: Dec 10, 2015

**Inventors**: Walter M. Weber (Laguna Hills, CA), Ammar Al-Ali (Tustin, CA), Lorenzo Cazzoli (Barcelona)

**Application Number**: 14/830,211

**Classifications**

**International Classification**: A61B 5/00 (20060101); A61B 5/021 (20060101); A61B 5/1455 (20060101);