SIGNAL PROCESSING METHOD AND APPARATUS

• US 20170332978A1
• Filed: 12/08/2015
• Published: 11/23/2017
• Est. Priority Date: 12/08/2014
• Status: Active Grant

• Current Assignee: Oxehealth Limited
• Original Assignee: Oxford University Innovation Limited (University of Oxford)
• Inventors: GUAZZI, Alessandro, SHAH, Syed Ahmar, TARASSENKO, Lionel
• Granted Patent: US 10,993,676 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 2576/00 Medical imaging apparatus involving image processing or analysis A61B1/00009, A61B6/52 and A61B8/52 take precedence; image processing per se G06T; details of algorithms for analysing medical images G06T7/0012
• CPC Class Codes: A61B 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/0836 Measuring rate of CO2 production
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/7214 using signal cancellation, e.g. based on input of two identical physiological sensors spaced apart, or based on two signals derived from the same sensor, for different optical wavelengths
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/7253 characterised by using transforms
• CPC Class Codes: A61B 5/7257 using Fourier transforms
• 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: G01R 23/167 with digital filters

Abstract

A method and apparatus for estimating the frequency of a dominant periodic component in an input signal by modelling the input signal using auto-regressive models of several different orders to generate candidate frequencies for the periodic component, generating synthetic sinusoidal signals of each of the candidate frequencies, and calculating the cross-correlation of the synthetic signals with the original signal. The frequency of whichever of the synthetic signals has the highest cross-correlation with the original signal is taken as the estimate of the frequency for the dominant periodic component of the input signal. The method may be applied to any noisy signal which has a suspected periodic component, for example physiological signals such as photoplethysmogram signals, and in the estimation of heart rate and breathing rate from such physiological signals.

Claims

1. A method of analysing an input signal to estimate the frequency of a periodic signal therein comprising the steps of:

receiving the input signal;

generating frequency domain candidates for the periodic signal;

for each frequency domain candidate generating a synthetic signal of the same frequency;

calculating in the time domain a measure of the similarity of each synthetic signal with the input signal;

outputting as the estimate of the frequency of the periodic signal the frequency of the synthetic signal with the maximum similarity with the input signal.

2. A method according to claim 1 wherein the measure of similarity is the correlation or the cross-correlation.

3. A method according to claim 1, wherein the frequency domain candidates are generated by performing spectral analysis on the input signal to detect its spectral components.

4. A method according to claim 3 wherein the spectral analysis is by autoregressive modelling.

5. A method according to claim 4 wherein a plurality of autoregressive models of different orders are fitted to the input signal.

6. A method according to claim 5 wherein the plurality of autoregressive models comprise models of order 2 to 20.

7. A method according to claim 4, wherein synthetic signals are generated for each pole in the fitted autoregressive models.

8. A method according to claim 4, wherein synthetic signals are generated for only poles in the fitted autoregressive models which correspond to a predetermined allowed frequency range for the periodic signal.

9. A method according to claim 4, wherein synthetic signals are generated for only poles in the fitted autoregressive models which have a predetermined magnitude.

10. A method according to claim 4, wherein synthetic signals are generated for only the dominant poles in the fitted autoregressive models.

11. A method according to claim 1 wherein the frequency domain candidates are generated by selecting a plurality of different frequencies within a predetermined frequency range.

12. A method according to claim 11 wherein the plurality of different frequencies are evenly spaced from each other over the frequency range.

13. A method according to claim 1 wherein the input signal is temporally windowed, for example from 5 to 60 seconds long, and the steps of calculating the similarity and outputting a frequency estimate are performed for each window.

14. A method according to claim 13 wherein the windows are overlapping windows, for example with an overlap from 0.5 to 10 seconds.

15. A method according to claim 1, wherein the input signal is obtained by detrending and filtering a noisy signal.

16. A method according to claim 15 wherein the input signal is obtained by resampling the detrended and filtered noisy signal.

17. A method according to claim 1, wherein the synthetic signal is sinusoidal.

18. A method according to claim 1, further comprising the step of defining a similarity threshold and inhibiting the outputting step if the similarity of the synthetic signal with the maximum similarity with the input signal is below the threshold.

19. A method according to claim 1, wherein the input signal is a time series of image intensity data.

20. A method according to claim 1 wherein the input signal is video image data comprising a time series of intensity data for each colour channel.

21. A method according to claim 1 wherein the input signal is a measurement of a physiological parameter of a human or animal subject.

22. A method according to claim 21 wherein the periodic signal is the heart rate or respiration rate of the subject.

23. A computer program comprising program code means for executing on a computer system the method of claim 1.

24. Apparatus for analysing an input signal to estimate the frequency of a periodic signal therein comprising:

an input for receiving the input signal;

a processor for processing input signal;

the processor being configured to execute the steps of claim 1;

the apparatus further comprising an output to output as the frequency estimate the frequency of the synthetic signal with the maximum cross-correlation with the input signal.