System for processing physiological data

• US 8,478,389 B1
• Filed: 04/22/2011
• Issued: 07/02/2013
• Est. Priority Date: 04/23/2010
• Status: Active Grant

• Current Assignee: VivaQuant LLC
• Original Assignee: VivaQuant LLC
• Inventors: Brockway, Marina, Brockway, Brian
• Primary Examiner(s): GETZOW, SCOTT M
• Application Number:
  US13/092,530
• Time in Patent Office: 802 Days
• Field of Search: 600/300, 600508-523
• US Class Current: 600/509
• CPC Class Codes: A61B 5/0006 ECG or EEG signals
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

A parameter value is computed for a segment of a cardiac-related signal. In accordance with various example embodiments, a system includes a computer circuit configured to identify cardiac cycles within a segment of a cardiac-related signal, such as an ECG. At least one feature point is identified within the cardiac cycles. For each identified feature point, a signal-to-noise ratio (SNR) representative of the ratio of signal energy to noise energy is computed for a cardiac cycle subsegment containing the identified feature point. A validity characteristic of the feature point is determined based upon the signal-to-noise ratio, and a parameter value is computed by combining feature points contained within the segment, based upon the determined validity characteristics of the feature points.

Claims

1. A system for computing a parameter value for a segment of a cardiac-related signal, the system comprising:

a computer circuit configured and arranged to

identify at least one cardiac cycle within said segment,

identify at least one feature point within the at least one cardiac cycle by

decomposing said cardiac-related signal into subcomponents,

identifying subcomponents of the cardiac-related signal associated with a signal wave containing the at least one feature point, and

combining said identified subcomponents to construct an emphasis signal, and evaluating the emphasis signal to identify the at least one feature point,

for the at least one identified feature point, compute an estimate of at least one of signal energy and noise energy for a cardiac cycle subsegment containing the at least one identified feature point, and determine a validity characteristic of the at least one feature point based upon the estimate, and

compute a parameter value based upon the determined validity characteristics of the at least one feature point.

2. The system of claim 1, wherein the computer circuit is configured and arranged to compute the estimate by computing the estimate for a cardiac cycle subsegment that is about 10% of the cardiac cycle.

3. The system of claim 1, wherein the computer circuit is configured and arranged to identify the at least one feature point by

evaluating said emphasis signal to identify at least one of a peak, valley, and inflection point as corresponding to the at least one feature point.

4. The system of claim 1, wherein the cardiac signal is an electrocardiogram (ECG) signal including a QRS complex, and the computer circuit is configured and arranged to

identify the at least one feature point by identifying one of a p-wave onset, t-wave onset, t-wave offset, and q-wave onset, and

compute the estimate for a cardiac cycle subsegment that excludes at least the QRS complex.

5. The system of claim 1, wherein the computer circuit is configured and arranged to identify the at least one cardiac cycle by identifying a plurality of cardiac cycles within the segment, and to determine the validity characteristic by computing the estimates for signal energy and noise energy for a subsegment including the identified feature point in each of the plurality of cardiac cycles and by combining the estimates for signal energy and noise energy.

6. The system of claim 1, wherein said cardiac-related signal is one of an ECG signal, blood pressure signal, photoplethysmography signal, heart sound signal, blood oxygen saturation signal and blood flow signal.

7. The system of claim 1, wherein the computer circuit is configured and arranged to compute an estimate of at least one of signal energy and noise energy for a cardiac cycle subsegment by computing the estimate for a cardiac cycle subsegment including at least two sample points consecutively before the identified feature point, and two sample points consecutively after the identified feature point.

8. The system of claim 1, wherein the computer circuit is configured and arranged to identify at least one feature point by identifying at least one of a peak, valley, and inflection point in the at least one cardiac cycle.

9. The system of claim 1, wherein the computer circuit is configured and arranged to

identify at least one feature point by identifying at least one of a peak, valley, and inflection point in the at least one cardiac cycle, and

compute an estimate of at least one of signal energy and noise energy for a cardiac cycle subsegment by computing the estimate for a cardiac cycle subsegment including at least two sample points consecutively before the identified feature point, and two sample points consecutively after the identified feature point.

10. A system for computing a parameter value for a segment of a cardiac-related signal, the system comprising:

a computer circuit configured and arranged to

identify at least one cardiac cycle within said segment,

identify at least one feature point of a signal wave within the at least one cardiac cycle, and

for the at least one identified feature point, compute the estimate an estimate of at least one of signal energy and noise energy for a cardiac cycle subsegment containing the at least one identified feature point, wherein computing at least one of the signal energy and noise energy includes

decomposing said cardiac-related signal into subcomponents,

identifying a subsegment of the at least one cardiac cycle that includes the at least one feature point,

when computing signal energy, estimating the signal energy by combining subcomponents within the subsegment that together contain a majority of the signal energy, and

when computing noise energy, estimating the noise energy by combining residual subcomponents that are not combined in estimating the signal energy;

determine a validity characteristic of the at least one feature point based upon the estimate; and

compute a parameter value based upon the determined validity characteristics of the at least one feature point.

11. The system of claim 10, wherein the subcomponents containing a majority of the signal energy are subcomponents that are within the frequency range of the spectrum of the signal wave.

12. An article of manufacture, comprising:

a processor-readable storage medium configured with configuration data that when executed by a processor, cause the processor to perform steps including;

identify cardiac cycles within a segment of a cardiac-related signal,

identify at least one feature point within said cardiac cycles,

for each of the identified feature points, compute a signal-to-noise ratio representative of the ratio of signal energy to noise energy for a cardiac cycle subsegment containing the identified feature point, and determine a validity characteristic of the feature point as a function of said signal-to-noise ratio, and

compute a parameter value by combining feature points contained within said segment based upon the determined validity characteristics of the feature points.

13. An apparatus comprising:

a computer circuit configured and arranged to

identify at least one cycle within an electrocardiogram signal,

identify a feature point within a subsegment of the at least one cycle by

decomposing said electrocardiogram signal into subcomponents,

computing an emphasis signal using one or more subcomponents that together contain at least a majority of energy of a signal wave within the subsegment, and

evaluating the emphasis signal to identify the feature point, and

compute a validity characteristic of the feature point based upon at least one of estimated signal energy and estimated noise energy within the subsegment.

14. The apparatus of claim 13, wherein the signal is a digital signal comprised of samples and the subsegment containing the identified feature point has a duration equal to about 10 samples.

15. The apparatus of claim 13, wherein the subsegment containing the identified feature point has a duration equal to about 25% of the cycle duration.

16. The apparatus of claim 13, wherein the subsegment containing the identified feature point has a duration equal to about 50% of the cycle duration.

17. The apparatus of claim 13, wherein the computer circuit is configured and arranged to compute the validity characteristic based upon a relationship between estimated signal energy and estimated noise energy within the subsegment.

18. The apparatus of claim 13, further including dividing the at least one cycle into at least two time windows based upon time-frequency characteristics, wherein computing the validity characteristic includes identifying a first time window corresponding to the subsegment containing the identified feature point, wherein the frequency range of signal spectral content in the first time window is lower than the frequency range of the signal spectral content of a window containing the QRS complex.

19. The apparatus of claim 13, wherein the identified feature point is one of p-wave onset, t-wave onset, t-wave offset, and q-wave onset, and the relationship between signal energy and noise energy is computed for a subsegment containing the identified feature point, said subsegment excluding at least the QRS complex of the cardiac cycle.

20. The apparatus of claim 13, wherein the computer circuit is configured to identify said feature point by

evaluating said emphasis signal to identify at least one of a peak, valley, and inflection point as corresponding to a feature point.

21. The apparatus of claim 13, wherein the computer circuit is further configured to compute the validity characteristic by combining signal and noise energy estimates from at least two cycles and determining a relationship between signal energy and noise energy from the combined values.

22. The apparatus of claim 13, wherein the apparatus is configured and arranged to identify at least one cycle within one of a blood pressure signal, photoplethysmography signal, blood oxygen saturation signal, blood flow signal, respiration signal, and heart sounds signal, and to identify the feature point and compute the validity characteristic based upon a subsegment of the identified at least one cycle.

23. The apparatus of claim 13, further comprising computing a parameter value for the electrocardiogram signal by combining at least two feature points based upon the validity characteristic.

24. An apparatus comprising:

a computer circuit configured and arranged to

identify at least one cycle within an electrocardiogram signal,

identify a feature point of a wave of the electrocardiogram signal within a subsegment of the at least one cycle,

compute at least one of estimated signal energy and estimated noise energy in the subsegment by

decomposing said electrocardiogram signal into subcomponents,

identifying a cycle,

identifying said subsegment containing said feature point,

when estimating signal energy, combining wave subcomponents from within said subsegment that, combined, represent at least a majority of the energy of said wave, and

when estimating noise energy, combining residual ones of the wave subcomponents that have not been combined in estimating the signal energy, and

compute a validity characteristic of the feature point based upon at least one of estimated signal energy and the estimated noise energy within the subsegment.

25. An article of manufacture, comprising:

a processor-readable storage medium configured with configuration data that when executed by a processor, cause the processor to perform steps including;

identify cycles within a segment of a pseudoperiodic physiological signal,

identify at least one feature point within said cycles,

for each of the identified feature points, compute an estimate of signal energy and noise energy in a cycle subsegment containing the identified feature point, and determine a validity characteristic of the feature point as a function of the relationship of said signal and noise energy estimates, and

compute a parameter value by combining feature points based upon the determined validity characteristics of the feature points.