Research On Blood Pressure Estimation from PPG using DL

This paper presents an innovative technique for the 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 mark health issues, adequate measurement of these parameters is required by a vast range of people. This project focuses on the fact that the measurement of these parameters can be effectively and accurately achieved through photoplethysmography (PPG) which is one of the non-invasive methods. The analysis of the PPG signals is also made to check 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, and diversity of topics, based on the datasets used over the period between 2010 – 2019. The data are pre-processed through normal Deep learning techniques and the algorithm of artificial intelligence and neural networks are applied to it. From this analysis, the accuracy of the data is also checked. All these methods are used for the continuous monitoring and evaluation of blood pressure and glucose level using PPG signals in a non-invasive way.