Performance of Biological Measurements in the Presence of Noise

• US 20170071546A1
• Filed: 06/15/2016
• Published: 03/16/2017
• Est. Priority Date: 07/16/2015
• Status: Active Grant

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: Jain, Jawahar, Young, James, Wortham, Cody, Sadi, Sajid, Mistry, Pranav
• Granted Patent: US 10,456,088 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 2503/10 Athletes
• CPC Class Codes: A61B 2560/0209 adapted for power saving
• CPC Class Codes: A61B 5/0006 ECG or EEG signals
• CPC Class Codes: A61B 5/0013 Medical image data A61B1/00011, A61B6/56, A61B8/56 take precedence
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• 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 5/0533 Measuring galvanic skin response
• CPC Class Codes: A61B 5/165 Evaluating the state of mind, e.g. depression, anxiety
• CPC Class Codes: A61B 5/352 Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/486 Bio-feedback A61B5/375 takes precedence
• CPC Class Codes: A61B 5/4884 inducing physiological or psychological stress, e.g. applications for stress testing
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/6833 Adhesive patches
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7225 Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation A61B5/30, A61B5/316 take precedence; transformers for specific applications H01F38/00; amplifiers H03F
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• 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 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• 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 5/7405 : using sound
• CPC Class Codes: A61B 5/742 : using visual displays A61B5/339 takes precedence
• CPC Class Codes: A61B 5/7455 : characterised by tactile indication, e.g. vibration or electrical stimulation
• CPC Class Codes: A61B 5/746 : Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61B 5/7475 : User input or interface means, e.g. keyboard, pointing device, joystick
• CPC Class Codes: G16H 40/60 : for the operation of medical equipment or devices
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 40/67 : for remote operation

Abstract

In particular embodiments, a method for determining a heart-rate variability (HRV) measurement of a user may include receiving, from a sensor of an electronic device of the user, heart sensor data, determining a noise level of the heart sensor data, and then adjusting the HRV measurement by an amount calculated based on the determined noise level. The sensor data may include data on a photoplethysmograph (PPG) signal over a noise-calculation time period.

Claims

1. A method for determining a heart-rate variability (HRV) measurement of a user, comprising:

receiving, from a sensor of an electronic device of the user, heart sensor data;

determining a noise level of the received heart sensor data; and

adjusting the HRV measurement by an amount calculated based on the determined noise level.

2. The method of claim 1, wherein the heart sensor data comprises data on a photoplethysmograph (PPG) signal over a noise-calculation time period.

3. The method of claim 2, wherein the determining of the noise level of the heart sensor data comprises:

calculating an error-noise estimation associated with the PPG signal; and

quantifying an amount of noise of the PPG signal based on the error-noise estimation associated with the PPG signal.

4. The method of claim 3, wherein the adjusting of the HRV measurement by the amount calculated based on the determined noise level comprises:

determining whether the amount of noise of the PPG signal is greater than a threshold amount of noise;

performing noise-compensation processing on the PPG signal when it is determined that the error-noise estimation is greater than the threshold amount of error; and

adjusting the HRV measurement of the user based on the processed PPG signal.

5. The method of claim 2, further comprising:

detecting a plurality of peaks of the processed PPG signal;

extracting a plurality of R wave-to-R wave (RR) time intervals based on the plurality of peaks of the processed PPG signal, wherein each RR interval corresponds to a time interval between each of two consecutive peaks of the processed PPG signal; and

calculating an error-adjustment measurement of the HRV measurement based on the extracted RR time intervals.

6. The method of claim 5, wherein the adjusting of the HRV measurement comprises decreasing the HRV measurement by the calculated error-adjustment measurement.

7. The method of claim 6, wherein the amount is noise of the PPG is quantified in real-time, and

wherein the HRV measurement is adjusted in approximately real-time.

8. The method of claim 2, wherein the quantifying of the amount of noise of the PPG signal comprises quantifying an amount of noise associated with each of a plurality of predetermined features of the PPG signal.

9. The method of claim 8, wherein the plurality of predetermined features of the PPG signal comprises:

one or more peaks associated with the PPG signal,

one or more troughs associated with the PPG signal,

one or more zero-crossings associated with the PPG signal, or

any combination thereof.

10. The method of claim 8, wherein when the amount of noise for a particular predetermined feature is determined to be greater than a threshold amount of noise for the particular predetermined feature, the particular predetermined feature is determined to be associated with one or more segments of noisy PPG signal.

11. The method of claim 10, further comprising:

interpolating, based on the received PPG signal, one or more statistical PPG waveforms for the noise-calculation time period; and

correcting the one or more segments of noisy PPG signal associated with one or more noisy time periods based on the statistical PPG waveforms.

12. The method of claim 11, the correcting of the segments of noisy PPG signal comprises blending the statistical PPG waveforms into the received PPG signal.

13. The method of claim 12, wherein the blending of the statistical PPG waveforms into the received PPG signal comprises:

weighing the statistical PPG waveforms higher at a beginning of each of the noisy time periods, and

decreasing the weight of the statistical PPG waveform with increasing time while concurrently increasing a weight of the received PPG signal.

14. The method of claim 13, wherein the statistical PPG waveforms are blended into the received PPG signal in approximately real time over the noise-calculation time period.

15. The method of claim 2, wherein, prior to sending the adjusted HRV measurement to the electronic device of the user, the method further comprises:

comparing the adjusted HRV measurement to a respiratory sinus arrhythmia (RSA) waveform to determine whether a difference is statistically significant,

wherein when the different is determined to be statistically significant, further adjusting the HRV measurement based on the RSA waveform.

16. The method of claim 2, further comprising sending, to the electronic device of the user, the adjusted HRV measurement.

17. The method of claim 2, further comprising:

storing historical data collected over the noise-calculation time period in a storage device, the historical data comprising the heart sensor data and one or more baseline data; and

analyzing the stored historical data to provide post-hoc analysis of the historical data.

18. The method of claim 17, further comprising auto-correcting noise associated with subsequent heart sensor data based on the post-hoc analysis of the historical data.

19. One or more computer-readable non-transitory storage media embodying software for determining a heart-rate variability (HRV) measurement of a user that is operable when executed to:

receive, from a sensor of an electronic device of the user, heart sensor data;

determine a noise level of the heart sensor data; and

adjust the HRV measurement by an amount calculated based on the determined noise level.

20. A system comprising:

one or more processors; and

a non-transitory memory coupled to the processors comprising instructions for determining a heart-rate variability (HRV) measurement of a user executable by the processors, the processors operable when executing the instructions to;

receive, from a sensor of an electronic device of the user, heart sensor data;

determine a noise level of the heart sensor data; and

adjust the HRV measurement by an amount calculated based on the determined noise level.

21. A method for measuring heart rate, comprising:

receiving, from a sensor of an electronic device of a user, heart sensor data;

analyzing an amount of noise at one or more predetermined features of the heart sensor data to determine whether the one or more features have been corrupted by noise; and

computing RR intervals of the heart rate based on one or more of the features that are determined to not have been corrupted by noise.

22. The method of claim 21, wherein the heart sensor data comprises data on a photoplethysmograph (PPG) signal.

23. The method of claim 22, wherein the one or more predetermined features of the PPG signal comprises:

one or more peaks associated with the PPG signal,

one or more troughs associated with the PPG signal,

one or more zero-crossings associated with the PPG signal, or

any combination thereof.

24. The method of claim 22, wherein the determining of whether the one or more features have been corrupted by noise comprises:

analyzing the amount of noise at each of the one or more predetermined features, and

analyzing an amount of noise in the vicinity of each of the one or more predetermined features, wherein the features that are determined to not have been corrupted by noise include features for which noise is determined to be below a minimum threshold.

25. The method of claim 22, wherein the computing of the RR intervals of the heart rate is further based on computing a mutual information value of the heart sensor data.