METHOD AND SYSTEM FOR SCREENING OF ATRIAL FIBRILLATION

• US 20150359443A1
• Filed: 08/21/2015
• Published: 12/17/2015
• Est. Priority Date: 11/01/2013
• Status: Active Grant

• Current Assignee: Verily Life Sciences LLC (Alphabet Inc.)
• Original Assignee: Cardiio, Inc.
• Inventors: Poh, Ming-Zher
• Granted Patent: US 9,913,588 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2576/023 for the heart
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/0261 using optical means, e.g. infrared light
• CPC Class Codes: A61B 5/0295 using plethysmography, i.e. measuring the variations in the volume of a body part as modified by the circulation of blood therethrough, e.g. impedance plethysmography
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• 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: G16H 30/40 for processing medical images, e.g. editing
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

The present disclosure provides a description of various methods and systems associated with determining possible presence of Atrial Fibrillation (AF). In one example, a camera of a client device, such as a mobile phone, may acquire a series of images of a body part of a user. A plethysmographic waveform may be generated from the series of images. An autocorrelation function may be calculated from the waveform, and a number of features may be computed from the autocorrelation function. Based on an analysis of the features, a determination may be made about whether the user is experiencing AF. Such determined may be output to a display of the mobile phone for user review.

Claims

1. A method for detecting presence of atrial fibrillation, the method comprising:

acquiring a plethysmographic waveform;

calculating an autocorrelation function of the plethysmographic waveform;

computing at least one feature from the autocorrelation function; and

analyzing, using a processor, the at least one feature from the autocorrelation function to determine presence of atrial fibrillation.

2. The method according to claim 1, wherein acquiring the plethysmographic waveform comprises acquiring the plethysmographic waveform using a sensor.

3. The method according to claim 2, wherein the sensor comprises a camera.

4. The method according to claim 3, wherein the camera comprises a camera with self-contained optics.

5. The method according to claim 3, wherein acquiring the plethysmographic waveform comprises:

a) capturing a series of images from a body part;

b) identifying a region of interest;

c) separating the series of images into one or more channels; and

d) averaging pixels within the region of interest.

6. The method according to claim 2, wherein the sensor comprises at least one light sensor configured to receive light from a light source.

7. The method according to claim 1, wherein computing the at least one feature comprises computing at least one feature from the group consisting of:

area-under-curve, a variation between autocorrelation coefficients, a complexity of the autocorrelation function, and an amplitude of the first non-zero-lag peak in the autocorrelation function.

8. The method according to claim 1, wherein the at least one feature comprises extracting a sequence of peak-amplitudes from the autocorrelation function using a peak detection algorithm.

9. The method according to claim 8, wherein the at least one feature further comprises at least one from the group consisting of:

computing time intervals between the peak-amplitudes of the autocorrelation function;

computing variation between peak-amplitudes of the autocorrelation function;

computing complexity between peak-amplitudes of the autocorrelation function;

computing variation of time-intervals between peaks of the autocorrelation function; and

computing complexity of peak-to-peak time-intervals of the autocorrelation function.

10. The method according to claim 8, wherein the at least one feature further comprises computing a strength of a monotone association between peak-amplitudes and corresponding lag times.

11. The method according to claim 1, wherein analyzing the at least one feature comprises comparing a value determined from the feature to a threshold value for the feature.

12. The method according to claim 11, wherein analyzing the at least one feature comprises analyzing a plurality of features, each of the features being compared to respective threshold values.

13. The method according to claim 1, wherein analyzing the at least one feature comprises analyzing a plurality of features using a classification function.

14. The method according to claim 1, further comprising displaying a result of the AF determination.

15. The method according to claim 14, further comprising displaying an AF probability.

16. A system for detecting presence of atrial fibrillation, the system comprising:

a sensor for acquiring a plethysmographic waveform;

a processor configured for calculating an autocorrelation function of the plethysmographic waveform, computing at least one feature from the autocorrelation function, and analyzing the at least one feature to determine presence of atrial fibrillation.

17. A method of detecting presence of atrial fibrillation at a client device, the method comprising:

capturing a series of images of at least a portion of a user;

generating a plethysmographic waveform from the series of images;

calculating an autocorrelation function of the plethysmographic waveform;

computing at least one feature from the autocorrelation function; and

analyzing, using a processor, the at least one feature from the autocorrelation function to determine presence of atrial fibrillation.

18. A method for monitoring the status of atrial fibrillation of an individual and providing feedback to a user, the method comprising:

acquiring a plethysmographic waveform;

calculating an autocorrelation function of the plethysmographic waveform;

computing at least one feature from the autocorrelation function;

analyzing, using a processor, the at least one feature from the autocorrelation function; and

providing at least one recommended course of action based upon the analysis of the at least one feature from the autocorrelation function.

19. The method according to claim 18, wherein the at least one recommended course of action includes at least one action from the group including:

scheduling an appointment with a care provider, consulting a care provider, adjusting medication levels, modifying diet, and modifying exercise patterns.

20. The method according to claim 18, wherein acquiring the plethysmographic waveform comprises acquiring the plethysmographic waveform using a sensor.

21. The method according to claim 20, wherein the sensor comprises a camera.

22. The method according to claim 21, wherein acquiring the plethysmographic waveform comprises:

a) capturing a series of images from a body part;

b) identifying a region of interest;

c) separating the series of images into one or more channels; and

d) averaging pixels within the region of interest.

23. The method according to claim 20, wherein the sensor comprises at least one light sensor configured to receive light from a light source.

24. The method according to claim 18, wherein computing the at least one feature comprises computing at least one feature from the group consisting of:

area-under-curve, a variation between autocorrelation coefficients, a complexity of the autocorrelation function, and an amplitude of the first non-zero-lag peak in the autocorrelation function.

25. The method according to claim 18, wherein the at least one feature comprises extracting a sequence of peak-amplitudes from the autocorrelation function using a peak detection algorithm.

26. The method according to claim 25, wherein the at least one feature further comprises at least one from the group consisting of:

computing time intervals between the peak-amplitudes of the autocorrelation function;

computing variation between peak-amplitudes of the autocorrelation function;

computing complexity between peak-amplitudes of the autocorrelation function;

computing variation of time-intervals between peaks of the autocorrelation function; and

computing complexity of peak-to-peak time-intervals of the autocorrelation function.

27. The method according to claim 25, wherein the at least one feature further comprises computing a strength of a monotone association between peak-amplitudes and corresponding lag times.

28. The method according to claim 18, wherein analyzing the at least one feature comprises comparing a value determined from the feature to a threshold value for the feature.

29. The method according to claim 28, wherein analyzing the at least one feature comprises analyzing a plurality of features, each of the features being compared to respective threshold values.

30. The method according to claim 18, wherein analyzing the at least one feature comprises analyzing a plurality of features using a classification function.