U.S. patent application number 14/924565 was filed with the patent office on 2016-08-04 for method for denoising and data fusion of biophysiological rate features into a single rate estimate.
The applicant listed for this patent is Samsung Electronics Co., LTD. Invention is credited to Asif KHALAK, Matthew C. WIGGINS.
Application Number | 20160223514 14/924565 |
Document ID | / |
Family ID | 56552652 |
Filed Date | 2016-08-04 |
United States Patent
Application |
20160223514 |
Kind Code |
A1 |
KHALAK; Asif ; et
al. |
August 4, 2016 |
METHOD FOR DENOISING AND DATA FUSION OF BIOPHYSIOLOGICAL RATE
FEATURES INTO A SINGLE RATE ESTIMATE
Abstract
A computer-implemented method for analyzing biophysiological
periodic data includes receiving a stream of feature data points,
determining whether each of the feature data points lies within or
outside a predetermined limit, and eliminating a first subset of
the feature data points in response to having determined that the
each of the data points in the first subset lies outside the
predetermined limit. The method further includes extracting a
feature from the feature data points that lie within the
predetermined limit over a time window, performing multiple
hypothesis tests to determine whether or not the feature
corresponds to a any of multiple hypothesis distributions, and
qualifying the feature as a qualified estimate of an actual feature
if the feature corresponds to statistical mean of a plurality of
recent qualified estimates.
Inventors: |
KHALAK; Asif; (Belmont,
CA) ; WIGGINS; Matthew C.; (San Jose, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Samsung Electronics Co., LTD |
Gyeonggi-DO |
|
KR |
|
|
Family ID: |
56552652 |
Appl. No.: |
14/924565 |
Filed: |
October 27, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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62110263 |
Jan 30, 2015 |
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62112032 |
Feb 4, 2015 |
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62113092 |
Feb 6, 2015 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/7253 20130101;
A61B 5/1455 20130101; A61B 5/7221 20130101; G01N 33/4833 20130101;
A61B 5/7207 20130101; A61B 5/0816 20130101; A61B 5/7203 20130101;
A61B 5/7264 20130101; A61B 5/0402 20130101; A61B 5/7225 20130101;
A61B 5/7278 20130101; A61B 5/02416 20130101 |
International
Class: |
G01N 33/483 20060101
G01N033/483; A61B 5/00 20060101 A61B005/00; A61B 5/08 20060101
A61B005/08; A61B 5/024 20060101 A61B005/024; A61B 5/1455 20060101
A61B005/1455 |
Claims
1. A device, comprising: a memory that stores machine instructions;
and a processor coupled to the memory that executes the machine
instructions to receive a plurality of feature data points, extract
a feature from a feature data point of the plurality of feature
data points that satisfy a predetermined range, perform a plurality
of hypothesis tests to determine whether the feature corresponds to
each of a plurality of predetermined hypothesis distributions
comprising a first hypothesis distribution, and qualify the feature
as a qualified estimate of an actual feature if the feature
corresponds to the first hypothesis distribution.
2. The device of claim 1, wherein the processor further executes
the machine instructions to determine a rate of change associated
with a first subset of the feature data points, and eliminate at
least one of the feature data points in response to having
determined that the rate of change is outside a predetermined rate
limit.
3. The device of claim 2, wherein the predetermined rate limit
comprises a confidence interval based on a second subset of the
feature data points that precedes the first subset in time.
4. The device of claim 1, wherein the processor further executes
the machine instructions to modify a first subset of the feature
data points to create a filtered subset of feature data points,
determine a rate of change associated with the filtered subset, and
eliminate at least one of the feature data points in response to
having determined that the rate of change is outside a
predetermined rate limit.
5. The device of claim 4, wherein the processor further executes
the machine instructions to implement an unscented Kalman filter to
create the filtered subset of feature data points.
6. The device of claim 1, wherein the first hypothesis distribution
represents a statistical mean of a plurality of recent qualified
estimates.
7. The device of claim 1, wherein the processor further executes
the machine instructions to modify the feature based on the feature
corresponding to a second hypothesis distribution, and qualify the
modified feature as a qualified estimate of an actual feature based
on the feature corresponding to the first hypothesis distribution,
wherein the plurality of hypothesis distributions further comprise
the second hypothesis distribution.
8. The device of claim 7, wherein the second hypothesis
distribution represents half of a statistical mean of a plurality
of recent qualified estimates.
9. The device of claim 1, wherein the processor further executes
the machine instructions to reject the feature if the feature
corresponds to a third hypothesis distribution, wherein the
plurality of hypothesis distributions further comprise the third
hypothesis distribution, which represents an artifact that is not
correlated with the actual feature.
10. A method, comprising: receiving a plurality of feature data
points; extracting a feature from a feature data point of the
plurality of feature data points that satisfy a predetermined
range; performing a plurality of hypothesis tests to determine
whether or not the feature corresponds to each of a plurality of
predetermined hypothesis distributions comprising a first
hypothesis distribution; and qualifying the feature as a qualified
estimate of an actual feature if the feature corresponds to the
first hypothesis distribution.
11. The method of claim 10, further comprising: determining a rate
of change associated with a first subset of the feature data
points; and eliminating at least one of the feature data points in
response to having determined that the rate of change is outside a
predetermined rate limit.
12. The method of claim 11, wherein the predetermined rate limit
comprises a confidence interval based on a second subset of the
feature data points that precedes the first subset in time.
13. The method of claim 10, further comprising: applying a filter
to a first subset of the feature data points to create a filtered
subset of feature data points; determining a rate of change
associated with the filtered subset; and eliminating at least one
of the feature data points in response to having determined that
the rate of change is outside a predetermined rate limit.
14. The method of claim 13, wherein the filter comprises an
unscented Kalman filter.
15. The method of claim 10, wherein the first hypothesis
distribution represents a statistical mean of a plurality of recent
qualified estimates.
16. The method of claim 10, further comprising: modifying the
feature if the feature corresponds to a second hypothesis
distribution; and qualifying the modified feature as a qualified
estimate of an actual feature if the feature corresponds to the
first hypothesis distribution, wherein the plurality of hypothesis
distributions further comprise the second hypothesis
distribution.
17. The method of claim 14, wherein the second hypothesis
distribution represents half of a statistical mean of a plurality
of recent qualified estimates.
18. The method of claim 10, further comprising rejecting the
feature if the feature corresponds to a third hypothesis
distribution, wherein the plurality of hypothesis distributions
further comprise the third hypothesis distribution.
19. The method of claim 16, wherein the third hypothesis
distribution represents an artifact that is not correlated with the
actual feature.
20. A computer program product, comprising: a non-transitory,
computer-readable storage medium encoded with instructions adapted
to be executed by a processor to implement: receiving a plurality
of feature data points; extracting a feature from a feature data
point of the plurality of feature data points that satisfy a
predetermined range; performing a plurality of hypothesis tests to
determine whether or not the feature corresponds to each of a
plurality of predetermined hypothesis distributions comprising a
first hypothesis distribution; and qualifying the feature as a
qualified estimate of an actual feature if the feature corresponds
to the first hypothesis distribution.
21. The computer program product of claim 20, wherein the
instructions are further adapted to implement: determining a first
rate of change associated with a first subset of the feature data
points; eliminating at least one of the feature data points in
response to having determined that the first rate of change is
outside a first predetermined rate limit; applying a filter to a
second subset of the feature data points to create a filtered
subset of feature data points; determining a second rate of change
associated with the filtered subset; and eliminating at least one
of the feature data points in response to having determined that
the second rate of change is outside a second predetermined rate
limit.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Application No. 62/110,263, filed Jan. 30, 2015; U.S. Provisional
Application No. 62/112,032, filed Feb. 4, 2015; and U.S.
Provisional Application No. 62/113,092, filed Feb. 6, 2015, which
are incorporated by reference herein.
TECHNICAL FIELD
[0002] This description relates generally to data analysis, and
more particularly to denoising and data fusion of biophysiological
rate features.
BACKGROUND
[0003] Data analysis generally encompasses processes of collecting,
cleaning, processing, transforming, and modeling data with the
goal, for example, of accurately describing the data, discovering
useful information or features among the data, suggesting
conclusions, or supporting decision-making. Data analysis typically
includes systematically applying statistical or logical techniques
to describe, condense, illustrate and evaluate data. Various
analytic techniques facilitate distinguishing the signal or
phenomenon of interest from unrelated noise and uncertainties
inherent in observed data.
[0004] Sensor data fusion techniques typically provide higher-level
information from data observed at multiple sensors, for example,
employing spatio-temporal data integration, exploiting redundant
and complementary information, as well as available context.
Exploratory data analysis often applies quantitative data methods
for outlier detection attempt to identify and eliminate inaccurate
data. In addition, descriptive statistics, such as the statistical
mean, median, variation or standard deviation may be generated to
help interpret the data. Further, data visualization may also be
used to examine the data in graphical format, providing insight
regarding the information embedded the data.
[0005] In general, statistical hypothesis testing, or confirmatory
data analysis, employs statistical inference to determine if a
result is significant based on a confidence interval or threshold
probability. Model selection techniques may be employed to
determine the most appropriate model from multiple hypotheses.
Decision theory and optimization techniques, including chi-square
testing, may further be employed to select the best of multiple
descriptive models. Statistical inference methods include, but are
not limited to, the Akaike information criterion (AIC), the
Bayesian information criterion (BIC), the focused information
criterion (FIC) the deviance information criterion (DIC), and the
Hannan-Quinn information criterion (HQC).
[0006] A photoplethysmogram (PPG) is an optically obtained
plethysmogram, or volumetric measurement of an organ. The pulse
oximeter, a type of PPG sensor, illuminates the skin with one or
more colors of light and measures changes in light absorption at
each wavelength. The PPG sensor illuminates the skin, for example,
using an optical emitter, such as a light-emitting diode (LED), and
measures either the amount of light transmitted through a
relatively thin body segment, such as a finger or earlobe, or the
amount of light reflected from the skin, for example, using a
photodetector, such as a photodiode. PPG sensors have been used to
monitor respiration and heart rates, blood oxygen saturation,
hypovolemia, and other circulatory conditions.
[0007] Conventional PPGs typically monitor the perfusion of blood
to the dermis and subcutaneous tissue of the skin, which may be
used to detect, for example, the change in volume corresponding to
the pressure pulses of consecutive cardiac cycles of the heart. If
the PPG is attached without compressing the skin, a secondary
pressure peak may also be seen from the venous plexus. A
microcontroller typically processes and calculates the peaks in the
waveform signal to count heart beats per minute (bpm).
[0008] However, signal noise from sources unrelated to desired
features, including, for example, motion artifacts and electrical
signal contamination, have proven to be a limiting factor affecting
the accuracy of PPG sensor readings. While the signal noise from
sources unrelated to desired features may be avoided in a clinical
environment, this signal noise may have an undesirable effect on
PPG sensor readings taken in free living conditions, for example,
during exercise. As a result, some existing data analysis
methodologies may have drawbacks when used with PPG sensor readings
taken in free living conditions.
SUMMARY
[0009] According to one embodiment, a device includes a memory that
stores machine instructions and a processor coupled to the memory
that executes the machine instructions to receive a plurality of
feature data points and extract a feature from a feature data point
of the plurality of feature data points that satisfy a
predetermined range. The processor further executes the machine
instructions to perform a plurality of hypothesis tests to
determine whether the feature corresponds to each of a plurality of
predetermined hypothesis distributions comprising a first
hypothesis distribution. If the feature corresponds to the first
hypothesis distribution, the processor further executes the machine
instructions to qualify the feature as a qualified estimate of an
actual feature.
[0010] According to another embodiment, a method includes receiving
a plurality of feature data points and extracting a feature from a
feature data point of the plurality of feature data points that
satisfy a predetermined range. The method further includes
performing a plurality of hypothesis tests to determine whether or
not the feature corresponds to each of a plurality of predetermined
hypothesis distributions comprising a first hypothesis
distribution. The method also includes qualifying the feature as a
qualified estimate of an actual feature if the feature corresponds
to the first hypothesis distribution.
[0011] According to yet another embodiment, a computer program
product includes a non-transitory, computer-readable storage medium
encoded with instructions adapted to be executed by a processor to
implement receiving a plurality of feature data points and
extracting a feature from a feature data point of the plurality of
feature data points that satisfy a predetermined range. The
instructions are further adapted to implement performing a
plurality of hypothesis tests to determine whether or not the
feature corresponds to each of a plurality of predetermined
hypothesis distributions comprising a first hypothesis
distribution. The instructions are also adapted to implement
qualifying the feature as a qualified estimate of an actual feature
if the feature corresponds to the first hypothesis
distribution.
[0012] The details of one or more embodiments of the present
disclosure are set forth in the accompanying drawings and the
description below. Other features, objects, and advantages of the
present disclosure will be apparent from the description and
drawings, and from the claims.
DESCRIPTION OF THE DRAWINGS
[0013] FIG. 1 illustrates a block diagram depicting an exemplary
biophysiological periodic data analyzer in accordance with an
embodiment.
[0014] FIG. 2 illustrates a flowchart of an exemplary method of
multiple-model adaptive estimation used to analyze biophysiological
periodic data in accordance with an embodiment.
[0015] FIG. 3 illustrates a graph depicting exemplary statistical
hypotheses for use in performing statistical inference regarding
feature data in accordance with an embodiment.
[0016] FIG. 4A illustrates a flowchart of an exemplary method of
analyzing biophysiological periodic data in accordance with an
embodiment.
[0017] FIG. 4B illustrates another flowchart of an exemplary method
of analyzing biophysiological periodic data in accordance with an
embodiment.
[0018] FIG. 4C illustrates another flowchart of an exemplary method
of analyzing biophysiological periodic data in accordance with an
embodiment.
[0019] FIG. 5 illustrates a schematic view depicting a computing
system that may be employed in a biophysiological periodic data
analyzer in accordance with an embodiment.
DETAILED DESCRIPTION
[0020] FIG. 1 illustrates a block diagram of an exemplary
biophysiological periodic data analyzer, according to one
embodiment. An biophysiological periodic data analyzer 10 includes
a feature receiver 12, a rate calculator 14, an outlier eliminator
16, a recent rate calculator 18, a rate filter 20, a rate change
computer 22, a biosemantic binary qualifier 24, a feature modifier
26, and a filter generator 28. The feature receiver 12 is
configured to receive multiple simultaneous data points from
various sensors monitoring biophysiological features of a subject,
including, but not limited to, a heart rate (HR), a respiration
rate, a fluid solution concentration, and a bodily movement. The
subject may include, but not limited to, a person, an animal, and a
living organism.
[0021] The data points include a data fusion from multiple sources
coming from different features on the same underlying sensors, or
different sensors. For example, the data points include feature
data regarding a subject's heart rate and respiration rate observed
over time using photoplethysmogram (PPG) sensors, such as pulse
oximeters. In one embodiment, the PPG sensor and the
biophysiological periodic data analyzer may be embedded in a
wearable device that is fastened to a subject, for example, the
subject's head, foot, finger, and wrist.
[0022] The feature receiver 12 sorts the monitored feature data
points and places the data points in order, for example,
feature-by-feature. The feature receiver 12 outputs each ordered
data point along with a synchronous time output. The rate
calculator 14 uses the most recent data point and a corresponding
time output to calculate the current feature rate based on a series
of recent data points.
[0023] The outlier eliminator 16 determines whether the current
feature rate falls within an acceptable range based on a set of
predetermined biological limits regarding the feature, for example,
minimum and maximum rate limits. A current feature rate that falls
outside the acceptable range are not used in further calculations.
The recent rate calculator 18 uses a series of current feature
rates within the acceptable range during a desired window of time
to calculate an updated recent feature rate.
[0024] The outlier eliminator 16 imposes constraints on the
hypotheses based on biophysiological limits. For example, a minimum
limit (`minHR`) and a maximum limit (`maxHR`) may be based on the
realistic expected range of human heart rates. Similarly, minimum
and maximum relative limits (`+/-deltaHR`) centered around the
recently observed heart rate value (uRecent) may be based on
physiological limitations regarding the rate of change of the heart
rate over the sampling time.
[0025] The rate filter 20 performs statistical calculations on
qualified feature data from the biosemantic binary qualifier 24,
which is further explained below. FIG. 2 illustrates a flowchart of
an exemplary method of multiple-model adaptive estimation (MMAE)
used to analyze biophysiological periodic data in accordance with
an embodiment. MMAE 30 may be implemented by the rate filter 20 to
analyze qualified feature data. In an embodiment, the rate filter
20 includes multiple Kalman filters, each based on a different
model. For example, a first Kalman filter 32 is based on a first
model, a second Kalman filter 34 is based on a second model, a
third Kalman filter 36 is based on a third model, and a fourth
Kalman filter 38 is based on a fourth model. Optionally, the
statistical calculations may implement weightings attached to the
data from each of the input streams, for example, indicating a
preference for information from one stream over that of another
stream. The rate change computer 22 continuously computes the
current rates of change regarding the filtered and unfiltered
rates.
[0026] The fusion at the hypothesis level follows an approach
equivalent to that used in the generic multiple-model adaptive
estimation framework, as described in the context of Kalman filters
by P. D. Hanlon and P. S. Maybeck in "Multiple-Model Adaptive
Estimation Using a Residual Correlation Kalman Filter Bank," IEEE
Transactions on Aerospace and Electronic Systems, Vol. AES-36, No.
2, April 2000, pp. 393-406, the entirety of which is incorporated
herein by reference. The Kalman filter estimation involves an
estimate and an uncertainty of the state of the system. For
instance, in an embodiment, an unscented Kalman filter associated
with alternate hypotheses of system behavior is used, which
explicitly fits a distribution from deterministic sampling of the
input, as described in Simon J. Julier & Jeffrey K. Uhlmann, "A
new extension of the Kalman filter to nonlinear systems", Int.
Symp. Aerospace/Defense Sensing, Simul. and Controls, vol. 3, p.
182, 1997, the entirety of which is incorporated herein by
reference.
[0027] The biosemantic binary qualifier 24 determines qualified
data, or qualifies data, based on a binary selection criterion for
each input feature, based on compatibility with learned
probabilistic models (many possible methods for model development).
The binary selection approach handles input data, even when there
is a large fraction of anomalies, or uncertainty, in the feature
data. The biosemantic binary qualifier 24 includes, for example, a
maximum likelihood decision engine. The biosemantic binary
qualifier 24 produces qualified data as output.
[0028] In an embodiment, the biosemantic binary qualifier 24 uses
the recent rate along with the filtered and unfiltered rates of
change to perform a hypothesis testing method 40. Multiple
hypothetical models are considered for each observed data point,
and the decision to accept the point is made based on a decision
rule for each hypothesis. The model hypotheses incorporate
biophysical limits on both on rates of change and the hard limits
on the values of the inputs, grounded in biophysiological
constraints. Each hypothesis transforms the input feature
differently, depending on the nature of the hypothesis.
[0029] FIG. 3 illustrates a graph depicting exemplary statistical
hypotheses for use in performing statistical inference regarding
feature data in accordance with an embodiment. A graph 50
illustrates various exemplary test hypotheses. Based on the window
statistics with respect to a particular time window, such as the
mean and standard deviation of the windowed rates, multiple
hypothetical probability models are trained, or developed. In an
embodiment, the test hypotheses consist of discrete expected
probability distributions, for example, including a recent
distribution 52, a trial distribution 54, and an artifact
distribution 56.
[0030] Referring to FIG. 3, the decision question is presented:
"Should a new beat 58 be accepted as a legitimate heart beat?" Two
exemplary hypotheses have been developed with respect to the heart
rate (HR), as follows: A first hypothesis, the recent distribution
52, presumes the measured input feature is consistent with the
recently observed heart rate. A second hypothesis, the trial
distribution 54, presumes the measured input feature has been
corrupted and is consistent with one-half the recently observed
heart rate. The second hypothesis is related to a specific sort of
signal corruption that gives an accurate estimate of one-half the
heart rate, which is grossly inaccurate for the true rate. A third
hypothesis, artifact distribution 56, presumes the measured input
feature has been corrupted and is consistent with an artifact that
is unrelated to the true heart rate. In other embodiments,
additional hypotheses may be included, for example, based on
characteristics of the input data stream.
[0031] The biosemantic binary qualifier 24 tests each of the
hypotheses on the basis of a probabilistic test. For instance, in
the case of the first hypothesis type described, both the recent
distribution 52 and the candidate point 58 are available.
Therefore, the computation of the posteriori likelihood of the
point being derived from the distribution is used to represent the
posteriori likelihood of the associated hypothesis.
[0032] Each hypothesis is considered independently--on the basis of
its own test against a null hypothesis. For instance, a hypothesis
is based on exceeding a threshold in a log-likelihood ratio test,
or in exceeding a threshold with respect to the affinity to the
distribution associated with the hypothesis. Following this, all
hypotheses which overcome the null hypothesis are ranked based on
an a priori ranking among hypotheses and the highest ranked
hypothesis is selected. This has the advantage that diverse
hypothesis types may be considered--some with an explicit
probability model for which likelihood may be computed, but others
using logical triggers for which no explicit probability model
exists.
[0033] Thus, these statistics are combined among the different data
sources, and then applied across each of the hypotheses.
Alternatively, separate statistics may be calculated associated
with each data type and these may be selectively attached to
different hypotheses.
[0034] In an alternate embodiment in which all of the hypotheses
have explicit probabilities, the hypothesis selection may then
proceed by computing the relative likelihood of each hypothesis
computed and selecting the most likely hypothesis is selected as
being correct. This triggers certain logic, as described below, to
either accept or to reject the candidate point.
[0035] For example, the feature data point may be accepted as
measured, based on a relatively high correlation to the hypothesis
associated with the recent distribution 52. Otherwise, the feature
modifier 26 may modify the feature data point before it is
accepted, for example, based on a relatively high correlation to
the hypothesis associated with the trial distribution 54. On the
other hand, the feature data point may be dropped from the output
stream, based on a relatively high correlation to the hypothesis
associated with the artifact distribution 56.
[0036] The filter generator 28 updates the rate filter 20 and
provides feedback to the biosemantic binary qualifier 24 to develop
the model hypotheses. The model hypotheses are stochastic
processes, which calculate the increases in uncertainty associated
with the time-sensitivity of information gathered. If no recent
feature data has been explained, the uncertainty grows. In an
embodiment, the statistics calculation implements, for example, a
Langevin correction. This modifies the probability model to account
for the time value of data by growing the model variance with the
time gap period. In an embodiment, the Langevin model, which is
based on physical models of Brownian motion, grows the model
variance linearly with time.
[0037] FIGS. 4A through 4C illustrate flowcharts of an exemplary
method of analyzing biophysiological periodic data in accordance
with an embodiment. Examples of biophysiological periodic data that
may be analyzed using the present method described in this
disclosure include, for example, a heart rate (HR), a respiration
rate, a fluid solution concentration, and a bodily movement. The
present method processes one or more streams of feature data
regarding a biophysiological feature over time and outputs a single
stream of qualified data.
[0038] Referring to FIG. 4A, input data tracks 62, 64, and 65 are
fed in order, feature-by-feature at 60. In one embodiment, the
features may include, for example, the interbeat interval of a
heart, a respiration rate, a step rate, and any other periodic
signal from a biophysiological sensor. A feature data stream is
separated into a sensed event at 68, and a corresponding time at
70. The output time at 70 is presented to a process that continues
at FIG. 4B, and the output rate, and/or output trial rate at 72 is
presented to processes that continue at FIGS. 4B and 4C. At 72, a
current rate (thisRate) associated with the sensed event and a
trial rate (trialRate) associated with a statistical hypothesis are
each calculated based on the event at 68.
[0039] A set of fixed, or absolute, biophysiological limits
regarding the features are received at 74, and a determination is
made at 76, regarding whether the rate and/or trial rate at 72 fall
within an acceptable range defined by the biophysiological limits.
If the rate and/or trial rate at 72 are found to be within the
acceptable range at 76, the process continues at 80 of FIG. 4B.
Otherwise, the rate and trial rate at 72 that fall outside the
acceptable range are discarded at 78. The biophysiological limits
are forwarded to the process at 80 of FIG. 4B.
[0040] Referring to FIG. 4B, if the rate and/or trial rate at 72
are found to be within the acceptable range at 76, the recent rate
based on statistics over a trailing window of time is updated at
80, based on the rate at 72 and the time at 70 in FIG. 4A. Data
points that fall outside the acceptable range at 76 of FIG. 4A are
trimmed from the input to the recent rate. At 82, the current rate
of change of the rate of block 72 is computed, resulting in a delta
rate (deltaRate) at 84. The recent rate calculated over a fixed
window of time is stored in a buffer, at 86.
[0041] In addition to the absolute limits applied at 76, the
present method also detects conditions in which limits on the
allowable rate of change have been exceeded. A dynamic limit
computed by the statistics of the recent time window, such as a
confidence interval. For example, a ninety-percent confidence
interval, a ninety-two-percent confident interval, or a
ninety-five-percent confidence interval is applied based on a
probabilistic model fit with respect to the previous window.
[0042] Statistical feedback data from FIG. 4C is used to modify the
recent rate filter (recentRateFilt), which is calculated over a
time window and stored in a buffer 88 as illustrated in FIG. 4B.
For example, the recent rate filter includes multiple Kalman
filters, as described above. The data fusion among the different
streams entering at the top of the block diagram of FIG. 4A is
managed in the calculation of statistics in the recent window at
88. Referring to FIG. 4B, at 90, the current rates of change of the
recent rate filter at 88 and the trial rate at 72 are computed,
resulting in a delta rate (deltaRateFilt)at 92.
[0043] Statistical hypothesis testing and data fusion are performed
at 94, for example, by a maximum likelihood decision engine
(biosemBinaryQualifier, or BBQ), to determine the event type based
on the biophysiological limits at 74, the recent rate at 86, the
delta rate at 84, the filter delta rate and the trial delta filter
rate at 92 and statistical feedback data at 112 from FIG. 4C. The
resultant event type at 96, is forwarded to the process at FIG.
4C.
[0044] Referring to FIG. 4C, based on the event type at 96 in FIG.
4B, decision logic at 100 determines the hypothesis category, for
example, type 0, type 1, or type 2. In an embodiment, the decision
rule (decision logic) may be framed as a question, for example,
"Should a newly observed feature (beat) be accepted as legitimate?"
The question may be answered probabilistically, for example based
on whether the feature lies within a certain confidence interval of
each of the hypotheses, or alternatively by computing the
chi-squared statistics associated with each of the hypotheses.
[0045] If the event type at 96 is determined to belong to a
hypothesis category, type 0, no further processing is performed
regarding the event type at 102. If the event type 96 is determined
to belong to a hypothesis category, type 1, the feature is passed
along without modification at 104. If the event type 96 is
determined to belong to the category, type 2, the feature is
modified according to a suitable model at 106.
[0046] At 108, the feature outputs at 104 and 106 are combined with
the time at 70 of FIG. 4A to produce a qualified feature with a
timestamp. The result for each timestamp is sent as an output at
110, for example, including a postqualified feature, the
corresponding hypothesis category or type. Optionally, a
corresponding weight may be included in the output.
[0047] In addition, in an alternative embodiment, the final result
may be temporally smoothed to improve the precision, albeit at the
expense of responsiveness. For example, the feature stream may be
estimated using various data smoothing approaches including, for
example, a boxcar moving average filter, an exponential moving
average filter, or the like. For example, the qualified feature
stream and the smoothed feature stream provide two estimates of the
true heart rate of a subject over time based on the measured heart
rate data represented by the feature data streams.
[0048] Statistical data is computed based on the qualified feature
with regard to a corresponding window of time at 112, and the
filter criteria is developed to update the recent rate filter at 88
in FIG. 4B. For example, a Langevin correction is made for time
gaps in the data streams. In an embodiment, all of the required
filtering criteria are determined at 112. A corollary output is
sent to a buffer at 114, for example, including statistics such as
the qualified feature mean and standard deviation with respect to
the time window corresponding to each timestamp. The windowed
statistics may be used, for example, to produce a confidence
measure on the output qualified feature stream.
[0049] As illustrated in FIG. 5, an exemplary computing device 120
may be employed in the biophysiological periodic data analyzer 10
of FIG. 1 includes a processor 122, a memory 124, an input/output
device (I/O) 126 storage 128 and a network interface 130. The
various components of the computing device 120 are coupled by a
local data link 132, which in various embodiments incorporates, for
example, an address bus, a data bus, a serial bus, a parallel bus,
or any combination of these.
[0050] The computing device 120 may be used, for example, to
implement the method of analyzing biophysiological periodic data of
FIG. 1. Programming code, such as source code, object code or
executable code, stored on a computer-readable medium, such as the
storage 128 or a peripheral storage component coupled to the
computing device 120, may be loaded into the memory 124 and
executed by the processor 122 in order to perform the functions of
the method of analyzing biophysiological periodic data of FIG.
1.
[0051] Aspects of this disclosure are described herein with
reference to flowchart illustrations or block diagrams, in which
each block or any combination of blocks may be implemented by
computer program instructions. The instructions may be provided to
a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
effectuate a machine or article of manufacture, and when executed
by the processor the instructions create means for implementing the
functions, acts or events specified in each block or combination of
blocks in the diagrams.
[0052] In this regard, each block in the flowchart or block
diagrams may correspond to a module, segment, or portion of code
that including one or more executable instructions for implementing
the specified logical function(s). It should also be noted that, in
some alternative implementations, the functionality associated with
any block may occur out of the order noted in the figures. For
example, two blocks shown in succession may, in fact, be executed
substantially concurrently, or blocks may sometimes be executed in
reverse order.
[0053] A person of ordinary skill in the art will appreciate that
aspects of this disclosure may be embodied as a device, system,
method or computer program product. Accordingly, aspects of this
disclosure, generally referred to herein as circuits, modules,
components or systems, may be embodied in hardware, in software
(including firmware, resident software, micro-code, etc.), or in
any combination of software and hardware, including computer
program products embodied in a computer-readable medium having
computer-readable program code embodied thereon.
[0054] It will be understood that various modifications may be
made. For example, useful results still could be achieved if steps
of the disclosed techniques were performed in a different order,
and/or if components in the disclosed systems were combined in a
different manner and/or replaced or supplemented by other
components. Accordingly, other implementations are within the scope
of the following claims.
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