Algorithm for the joint analysis of the ECG and PPG signals

Abstract

The main objective of this research is based on finding out some assertive and robust Photoplethysmogram’s PPG & Electrocardiogram’s ECG blood pressure-related parameters by the implementation of a novel method with innovations in signal processing and analysis. The biomedical ECG and PPG signals are recorded using a mobile monitor CardioQVark. To increase the cuffless blood pressure measurement accuracy, a technique that involves not only the ECG and PPG joint parameters extraction but also some individual PPG’s morphology features, is proposed in this work. Firstly, the biomedical ECG and PPG signals are time–frequency filtered. Secondly, some novel parameters from the morphology of photoplethysmogram signal, which may be correlated with blood pressure, are considered in addition to the pulse transit time. Additionally, a neural network is built to determine the relationship between the estimated and reference blood pressure. Finally, the correlation coefficient and regression line are obtained to evaluate the feasibility.