HEMODYNAMIC RISK SEVERITY BASED UPON DETECTION AND QUANTIFICATION OF CARDIAC DYSRHYTHMIA BEHAVIOR USING A PULSE VOLUME WAVEFORM

• US 20140357995A1
• Filed: 06/04/2014
• Published: 12/04/2014
• Est. Priority Date: 06/04/2013
• Status: Active Grant

• Current Assignee: Intelomed, Inc.
• Original Assignee: Intelomed, Inc.
• Inventors: Berkow, Jan K., Brumfield, Anne M.
• Granted Patent: US 10,390,767 B2
• Time in Patent Office: Days
• US Class Current: 600/438
• CPC Class Codes: A61B 5/02108 from analysis of pulse wave characteristics
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/361 Detecting fibrillation
• CPC Class Codes: A61B 5/363 Detecting tachycardia or bradycardia
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/742 using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 8/02 Measuring pulse or heart rate
• CPC Class Codes: A61B 8/5223 for extracting a diagnostic or physiological parameter from medical diagnostic data for algorithms to analyse biomedical images G06T7/0012

Abstract

A method for identifying cardiac dysrhythmia behavior may include acquiring pulse volume wave data from a sensor associated with a patient, and calculating metrics associated with peaks detected therein. The metrics may include differences in amplitudes of successive pulse volume peaks and differences in the times of occurrence of successive pulse volume peaks. A dispersion analysis of the time differences, obtained during a defined time window, may result in one or more time difference dispersion metrics. Amplitude differences may be compared to an amplitude baseline, and time differences may be compared to a time baseline. Cardiac dysrhythmia behavior may be identified by a combination of an amplitude difference outside of the amplitude baseline, a corresponding time difference outside of the time baseline, and the values of one or more time difference dispersion metrics.

Claims

1. A method for identifying a cardiac dysrhythmia behavior, the method comprising:

receiving, by a computing device, a biological signal emulating an arterial pulse wave from a sensor in data communication with a human body;

identifying, by the computing device, a plurality of signal peaks within the biological signal;

identifying, by the computing device, a peak amplitude for each of the plurality of signal peaks;

identifying, by the computing device, a time occurrence for each of the plurality of signal peaks;

calculating, by the computing device, a plurality of amplitude differences, wherein each amplitude difference of the plurality of amplitude differences is calculated from a first peak amplitude of a first peak and a second peak amplitude of a second peak;

calculating, by the computing device, a plurality of time differences, wherein each time difference of the plurality of time differences is calculated from a first time occurrence of the first peak and a second time occurrence of the second peak;

calculating, by the computing device, at least one time difference dispersion metric from the plurality of time differences; and

identifying, by the computing device, a cardiac dysrhythmia behavior of the biological signal from the at least one time difference dispersion metric in response to at least one anomalous amplitude difference calculated from a first anomalous peak and a second anomalous peak exceeds an amplitude threshold and at least one anomalous time difference calculated from the first anomalous peak and the second anomalous peak exceeds a time threshold.

2. The method of claim 1, wherein the sensor is a pulse volume detection sensor.

3. The method of claim 1, wherein the sensor is one or more of a plethysmograph, a transmittance photo-optic sensor, a reflective photo-optic sensor, a pressure transducer, a tonometry device, a strain gauge, an ultrasound device, an electrical impedance measurement device, and a radar device.

4. The method of claim 1, wherein the sensor is a photoplethysmograph.

5. The method of claim 1, wherein identifying a plurality of signal peaks comprises fitting at least a portion of the biological signal to a mathematical model.

6. The method of claim 1, wherein the second peak amplitude is the peak amplitude occurring immediately after the first peak amplitude.

7. The method of claim 1, wherein the second time occurrence is the time occurrence occurring immediately after the first time occurrence.

8. The method of claim 1, further comprising calculating, by the computing device, the amplitude threshold.

9. The method of claim 8, wherein calculating the amplitude threshold comprises:

calculating, by the computing device, an amplitude difference baseline from at least a portion of peak amplitude differences occurring within a data window; and

adding, by the computing device, an amplitude difference offset to the amplitude difference baseline to yield the amplitude threshold.

10. The method of claim 9, wherein calculating the amplitude difference baseline comprises calculating, by the computing device, an average of peak amplitude differences of at least a portion of peak amplitude differences occurring within the data window.

11. The method of claim 9, wherein calculating the amplitude difference baseline comprises calculating, by the computing device, a maximum peak amplitude difference of at least a portion of peak amplitude differences occurring within the data window.

12. The method of claim 9, wherein the amplitude difference offset is calculated from an average of peak amplitude differences.

13. The method of claim 9, wherein the amplitude threshold equals about 1.05 to about 1.5 times the amplitude difference baseline.

14. The method of claim 9, wherein the amplitude threshold equals about 1.2 times the amplitude difference baseline.

15. The method of claim 8, further comprising selecting, by the computing device an algorithm for calculating the amplitude threshold based on a value of the at least one time difference dispersion metric.

16. The method of claim 1, further comprising calculating, by the computing device, the time threshold.

17. The method of claim 16, wherein calculating the time threshold comprises:

calculating, by the computing device, a peak time difference baseline from at least a portion of peak time differences occurring within a data window; and

adding, by the computing device, a peak time difference offset to the peak time difference baseline to yield the time threshold.

18. The method of claim 17, wherein calculating the peak time difference baseline comprises calculating, by the computing device, an average of a reciprocal of a normative pulse rate derived from the human body.

19. The method of claim 17, wherein the peak time difference offset is calculated from the peak time difference baseline.

20. The method of claim 17, wherein the time threshold equals about 1.05 to about 1.5 times the peak time difference baseline.

21. The method of claim 17, wherein the time threshold equals about 1.2 times the peak time difference baseline.

22. The method of claim 1, further comprising calculating, by the computing device, the at least one time difference dispersion metric from the plurality of time differences.

23. The method of claim 22, wherein calculating the least one time difference dispersion metric comprises:

calculating, by the computing device, a histogram from at least a portion of the plurality of time differences; and

calculating, by the computing device, the at least one time difference dispersion metric from the histogram.

24. The method of claim 23, wherein the portion of the plurality of time differences occur within a data window.

25. The method of claim 24, wherein the data window has a window time of about 5 minutes to about 24 hours.

26. The method of claim 23, wherein the at least one time difference dispersion metric comprises one or more of a maximum value of at least one histogram peak, a value of a width metric of the at least one histogram peak, and a histogram difference time corresponding to the maximum value of the at least one histogram peak.

27. The method of claim 1, wherein identifying a type of cardiac dysrhythmia behavior comprises classifying a type of cardiac dysrhythmia behavior according to an arrhythmia grading system.

28. The method of claim 27, wherein the arrhythmia grading system comprises one or more of a Lown grading system, a Bigger grading system, a Morganroth grading system, or a combination thereof.

29. The method of claim 1, further comprising displaying, by the computing device on an output device, a representation of a portion of the biological signal along with at least one annotation identifying the cardiac dysrhythmia behavior.

30. The method of claim 29, wherein displaying the representation of the portion of the biological signal comprises updating the representation of the portion of the biological signal over time.

31. The method of claim 29, wherein the annotation is an arrhythmia indicator.

32. The method of claim 29, wherein the annotation is an indicator of one or more criteria from a Lown grading system, a Bigger grading system, a Morganroth grading system, or a combination thereof.

33. The method of claim 1, further comprising issuing, by the computing device, a warning to a user if the cardiac dysrhythmia behavior indicates an emergent condition associated with the human body.