HARMONIC TEMPLATE CLASSIFIER

• US 20160051158A1
• Filed: 11/21/2014
• Published: 02/25/2016
• Est. Priority Date: 08/22/2014
• Status: Abandoned Application

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: SILVA, James M.
• Application Number:
  US14/550,820
• Publication Number:
  US 20160051158A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/721 using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity

Abstract

An algorithm for removing motion artifacts from a PPG signal in the frequency domain and a harmonic template classifier (HTC) algorithm and unit to determine heart rate are disclosed. In some examples, PPG signals can be processed in combination with accelerometer signals to remove unwanted artifacts in the frequency domain. For example, an acceleration mask can be generated and used to filter out acceleration contributions represented in the PPG signal. Additionally or alternatively, in some examples, an HTC unit can be configured to generate a heart rate correlation curve based on the correlation between frequency domain PPG signals and spectral templates. In some examples, the HTC unit can be configured to implement an algorithm to determine a predicted heart rate and an associated confidence measure. In some examples, heuristics can be used to determine a predicted heart rate based on the correlation curve and/or the confidence measure.

Claims

1. A device for predicting heart rate, comprising:

a sensor configured to generate first signals; and

processing circuitry capable of;

generating a frequency domain representation of the first signals;

correlating the frequency domain representation of the first signals and a plurality of spectral heart rate templates to generate a plurality of correlation values; and

predicting a heart rate based on the plurality of correlation values.

2. The device of claim 1, further comprising:

a memory coupled to the processing circuitry and configured to store the plurality of spectral heart rate templates, wherein the processing circuitry is further capable of accessing the plurality of spectral heart rate templates stored in the memory.

3. The device of claim 1, wherein the processing circuitry is further capable of identifying one or more maxima in the plurality of correlation values, wherein one or more frequencies associated with the one or more maxima are candidates for predicting the heart rate.

4. The device of claim 3, wherein the processing circuitry is further capable of generating one or more confidence measures, the one or more confidence measures corresponding to the one or more maxima.

5. The device of claim 4, wherein generating a confidence measure includes calculating a mean of the correlation values and calculating a ratio of a correlation value at a maxima to the mean of the correlation values.

6. The device of claim 4, wherein the processing circuitry is further capable of applying one or more heuristics for predicting the heart rate based on the correlation values and the one or more confidence measures.

7. The device of claim 1, further comprising:

a sensor configured to generate acceleration signals;

wherein the processor is further capable of;

generating a frequency domain representation of the acceleration signals; and

removing motion artifacts from the frequency domain representation of the first signals using the frequency domain representation of the acceleration signals.

8. The device of claim 7, wherein removing the motion artifacts further includes truncating the frequency domain representations of the acceleration signals and first signals to exclude information outside a frequency range of interest.

9. The device of claim 7, wherein the processor is further capable of:

determining a mean of the frequency domain representation of the acceleration signals;

generating acceleration mask signals based on the frequency domain representation of the acceleration signals and the mean of the frequency domain representation of the acceleration signals; and

filtering the frequency domain representation of the first signals using the acceleration mask signals to remove the motion artifacts.

10. A method executed by processing circuitry for predicting a heart rate, the method comprising:

receiving first signals generated by a sensor;

generating a frequency domain representation of the first signals;

correlating the frequency domain representation of the first signals and a plurality of spectral heart rate templates to generate a plurality of correlation values; and

predicting a heart rate based on the plurality of correlation values.

11. The method of claim 10, further comprising generating a spectral heart rate template for a given heart rate by synthesizing a composite time domain waveform including a plurality of periodic components at one or more frequencies corresponding to the given heart rate and transforming the composite time domain waveform into the frequency domain.

12. The method of claim 11, further comprising determining a number of coefficients for the spectral heart rate template for a given heart rate by the number of coefficients that occur within a frequency range of interest.

13. The method of claim 10, further comprising identifying one or more maxima in the plurality of correlation values, wherein one or more frequencies associated with the one or more maxima are candidates for predicting the heart rate.

14. The method of claim 13, further comprising generating one or more confidence measures, the one or more confidence measures corresponding to the one or more maxima.

15. The method of claim 14, further comprising:

calculating a mean of the correlation values; and

generating one or more confidence measures based on a ratio of a correlation value at one or more maxima to the mean of the correlation values.

16. The method of claim 14, further comprising applying one or more heuristics for predicting the heart rate based on the correlation values and the one or more confidence measures.

17. The method of claim 16, wherein the one or more heuristics includes comparing a confidence measure to a confidence measure threshold.

18. The method of claim 16, wherein the one or more heuristics includes comparing a frequency associated with a maximum in the correlation values with one or more recent heart rate predictions.

19. The method of claim 16, wherein the one or more heuristics includes calculating a centroid of the correlation values.

20. The method of claim 16, wherein the one or more heuristics includes determining a modality of the correlation values.

21. The method of claim 16, wherein the one or more heuristics includes windowing the correlation values around a last known heart rate prediction.

22. The method of claim 10, further comprising:

receiving acceleration signals from an accelerometer;

generating a frequency domain representation of the acceleration signals; and

removing motion artifacts from the frequency domain representation of the first signals using the frequency domain representation of the acceleration signals.

23. The method of claim 22, further comprising:

truncating the frequency domain representations of the acceleration signals and first signals to exclude information outside a frequency range of interest.

24. The method of claim 22, further comprising:

determining a mean of the frequency domain representation of the acceleration signals;

generating acceleration mask signals based on the frequency domain representation of the acceleration signals and the mean of the frequency domain representation of the acceleration signals; and

filtering the frequency domain representation of the first signals using the acceleration mask signals to remove the motion artifacts.

25. A non-transitory computer readable storage medium, the computer readable medium containing instructions that, when executed, perform a method for operating an electronic device, the electronic device including a processor, the method comprising:

receiving first signals generated by a sensor when positioned on or adjacent to a user'"'"'s skin;

generating a frequency domain representation of the first signals;

correlating the frequency domain representation of the first signals and a plurality of spectral heart rate templates to generate a plurality of correlation values; and

predicting a heart rate of user based on the plurality of correlation values.