Blood pressure estimation method based on spatiotemporal features of electrocardiogram and photoplethysmography

Abstract

The present disclosure relates to a method of extracting spatiotemporal features of electrocardiogram (ECG) and photoplethysmography (PPG) signals corresponding to each other using a neural network and of estimating blood pressure on the basis of the spatiotemporal features. The method includes: generating a target signal of a plurality of channels by respectively combining ECG signals and PPG signals corresponding to each other; extracting a first feature composed of a plurality of channels by inputting the target signal of a plurality of channels into a 1D convolution layer; generating a channel-wise weight vector by compressing the first feature; computing a second feature composed of a plurality of channels by applying the channel-wise weight vector to the first feature; extracting a third feature by inputting the second feature into a CNN model; and determining systolic and diastolic blood pressures by inputting the third feature into an LSTM model.

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