METHOD AND APPARATUS FOR PHYSIOLOGICAL MONITORING

• US 20170347967A1
• Filed: 12/08/2015
• Published: 12/07/2017
• Est. Priority Date: 12/08/2014
• Status: Active Grant

• Current Assignee: Oxehealth Limited
• Original Assignee: Oxehealth Limited
• Inventors: GUAZZI, Alessandro
• Granted Patent: US 10,448,900 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 2562/0233 Special features of optical sensors or probes classified in A61B5/00
• 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 5/0075 by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy A61B5/0071 takes precedence
• CPC Class Codes: A61B 5/0077 Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02427 Details of sensor
• 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 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/1102 Ballistocardiography
• CPC Class Codes: A61B 5/1128 using image analysis A61B5/1127 takes precedence
• CPC Class Codes: A61B 5/113 occurring during breathing
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• 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: G06V 10/56 relating to colour

Abstract

Autoregressive modelling is used to identify periodic physiological signals such as heart rate or breathing rate in an image of a subject. The colour channels of a video signal are windowed and normalised by dividing each signal by its mean. The ratios of the normalised channels to each other are found and principal component analyses conducted on the ratio signals. The most periodic of the principal components is selected and autoregressive models of one or more different orders are fitted to the selected component. Poles of the fitted autoregressive models of different orders are taken and pure sinusoids corresponding to the frequency of each pole are generated and their cross-correlation with the original component is found. Whichever pole corresponds to the sinusoid with the maximum cross-correlation is selected as the best estimate of the frequency of periodic physiological information in the original video signal. The method may be used in a patient monitor or in a webcam-enabled device such as a tablet computer or smart phone.

Claims

1. A method of detecting the frequency of a periodic physiological signal of a subject from a video image of the subject, comprising the steps of:

fitting autoregressive models of one or more orders to a signal derived from the video image to detect spectral components in the signal;

for each spectral component generating a synthetic signal of the same frequency and calculating its similarity in the time domain with the signal derived from the video image;

outputting as the physiological signal frequency the frequency of the synthetic signal with the maximum similarity with the signal derived from the video image.

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

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

4. 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 signal derived from the video image is below the threshold.

5. A method according to claim 1 wherein the signal derived from the video image is a single colour channel of video source data.

6. A method according to claim 1 wherein the signal derived from the video image is a sequence of co-ordinates obtained by tracking physical movement in the image.

7. A method according to claim 1, wherein the signal derived from the video image is a ratio of two colour channels of video source data.

8. A method according to claim 1, wherein the signal derived from the video image is a sequence of co-ordinates of a physical feature being tracked.

9. A method according to claim 1, wherein the signal derived from the video image is derived by the following steps:

receiving multiple colour channels of video source data representing said video image, each channel comprising a time series of intensity data for that colour channel;

for each different pairing of the colour channels calculating the ratio of the intensity at each time point in a first of the pairing to the intensity at the same time point in the second of the pairing to produce multiple ratio signals;

performing source separation on the ratio signals and selecting the output component which is most periodic.

10. A method according to claim 1, wherein the signal derived from the video image is derived by the following steps:

receiving multiple sequences of co-ordinates of one or more physical features being tracked;

performing source separation on the sequences and selecting the output component which is most periodic.

11. A method according to claim 9 wherein the source separation is by Principal Component Analysis.

12. A method according to claim 9 wherein the selection of the most periodic output component is by selecting the component having the greatest peakiness of frequency spectrum.

13. A method according to claim 1 wherein autoregressive models of order 7 to 11 are fitted to the signal derived from the video image.

14. A method according to claim 1 wherein a synthetic signal is generated corresponding to the frequency of only the dominant spectral component for each order model.

15. A method according to claim 9 wherein the colour channels are red, green and blue and the pairings are red and green, red and blue, green and blue.

16. A method according to claim 7 wherein each colour channel is normalised by dividing by its mean before the step of calculating the ratio.

17. A method according to claim 1 wherein the signal derived from the video image is temporally windowed.

18. A method according to claim 17 wherein the signal derived from the video image is temporally windowed into overlapping windows.

19. A method according to claim 18 wherein the windows are from 4-30 seconds long and the overlap is from 0.5 to 10 seconds.

20. A method according to claim 1 wherein the video source data is a time series of intensity data for each colour channel for a region of interest defined in the video image.

21. A method according to claim 20 wherein the region of interest is on the skin of the subject and the video source data is a photoplethysmographic image.

22. A method according to claim 20 wherein the region of interest includes a periodically moving part of subject.

23. A method according to claim 1 wherein the periodic physiological signal is the heart rate or respiration rate of the subject.

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

25. Apparatus for detecting the frequency of a periodic physiological signal of a subject from a video image of the subject comprising:

an input for receiving one or more colour channels of video source data representing said video image, each channel comprising a time series of intensity data for that colour channel;

a processor for processing video source data;

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

the apparatus further comprising an output to output as the physiological signal frequency the frequency of the synthetic signal with the maximum cross-correlation with the selected ratio signal.