Skin Video-based Blood Pressure Approximation Using CHROM with LSTM-NN

Abstract

The measurement of blood pressure (BP) is an essential step in clinical practice. It is used to determine the patient’s BP, which reflects the condition of the patient. Recently, there is a solution for extracting, non-invasively and with no contact, a blood pressure indicator from electrical signal like Photoplethysmography (PPG), called remote-Photoplethysmography (rPPG). This rPPG signal can be used to estimate from a video clip several vital physiological indicators for humans, especially, systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP). This paper proposed a computer method for blood pressure approximation from an input video. A chrominance method, or CHROM, was used to extract rPPG signal from a given video before forwarding it to estimate SBP and DBP values by LSTM-NN. Afterwards, MAP value was determined from SBP and DBP values by a weighting score technique. Experimental results showed that CHROM achieved the lowest mean absolute error (MAE) at 14.04, 8.37, and 9.78 for the SBP, DBP, and MAP, respectively, when compared among NN, RNN, and GRU.