Review of PPG signal using Machine Learning Algorithms for Blood Pressure and Glucose Estimation

Abstract

This paper presents an innovative technique for current non-invasive measurement of systolic(SBP) and diastolic (DBP) blood pressures and BGL (blood glucose level). Blood glucose and blood pressure are the most significant factors which marks the health issues, adequate measurement of these parameters are requires by a vast range of people. This paper focuses that the measurement of these parameters can effectively and accurately achieved through photoplethysmography(PPG) which is the one among the non-invasive methods. The analysis of the PPG signals are also made to the check of the accuracy of the device.This review paper focuses on understanding the BP-related features from PPG and explores the growth of this technology in terms of validation, sample size, diversity of topics, based on the datasets used over the period between 2010 - 2019. The data are preprocessed through the normal machine learning techniques and the algorithm of artificial intelligence and neural networks are applied into it. From this analysis, the accuracy of the data is also checked into. All these methods are used for the continuous monitoring and evaluation of the blood pressure and glucose level using PPG signals in a non-invasive ways.