DEEP LEARNING APPROACH FOR HEART RATE PREDICTION USING PPG SIGNAL

Abstract

Photoplethysmography (PPG) is a low-cost optical device that measures changes in blood volume in the microvascular tissue bed from the skin’s surface. It has been used in commercial medical devices to gauge peripheral vascular disease and autonomic function by monitoring blood pressure, heart rate, and oxygen saturation. Due to the presence of motion artifact during exercises, there arises difficulty in measuring heart rate from PPG signal. A machine learning based approach is used to monitor heart rate (HR) using wrist-type photoplethysmography (PPG) signals in this paper. By combining 1D CNN and a bidirectional LSTM, the model get benefit from the strengths of both architectures, capturing both local and long-term patterns in the input data. The proposed model exhibits average absolute error of less than 1.5 bpm for all the training and test datasets. The model shows the promising result with less than 300 thousands network parameters.