Cardiovascular Diseases Detection Using Photo Plethysmography (PPG) Signal Data

Abstract

Photoplethysmography (PPG) signals have been widely used in clinical practice as diagnostic
tools. In this article, techniques of machine learning have been used to improve the detection
of cardiovascular disease (CVD) from the PPG signal data. Hypertension and stress are the
main causes of the increase in blood pressure (BP), which in turn causes cardiovascular
diseases. The treatment of patients, mainly those who have been suffering from CVD, resulted
in an increment in the death rate. PPG is non-invasive, low-cost, fast, and simple to use. The
signals of PPG are used for figuring out the anomalies in the cardiovascular system. By using
PPG technology, cardiovascular parameters like blood pressure and heart rate are detected. This
article investigates a machine learning and Deep Learning technique, which is Neural Network
(NN), that has been used to assist physicians, this has achieved an accuracy of 98% by using
the PPG-BP data set.