Oxford University Innovation Limited

Oxehealth Limited

Alessandro Guazzi

Lionel Tarassenko

Syed Ahmar Shah

Mauricio Villarroel Montoya

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.

Signal processing method and apparatus

A method of monitoring changes in oxygen saturation of a subject by analysing a three colour channel video image of the exposed skin of the subject. Within each colour channel a normalised signal obtained by dividing the intensity signal by its mean value, and the normalised signals are averaged across plural regions of interest within the exposed skin area image of the subject. Regions of interest are selected on the basis of the signal-to-noise ratios for the heart rate and breathing rate components. A single representative waveform for each colour channel is obtained by signal averaging and the ratio of the amplitudes of the representative waveforms from two different colour channels, e.g. blue and red, is taken. The changes in the ratio of amplitudes is output as a measure of changes in blood oxygen saturation.

Method and apparatus for vital signs measurement

Autoregressive modelling is used to identify periodic physiological signals such as heart rate or breathing rate in an image of a subject. The color 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.

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Oxford, United Kingdom

Alessandro Guazzi