Medical PPG Sensor for Cardiovascular Disease Diagnosis Using Personalized Hemodynamics Model and Pulse Wave Analysis

Abstract

Utilizing the medical photoplethysmography (PPG) sensor, this study presents the development of a hemodynamic model of the human body to diagnose cardiovascular disease. Through advanced signal processing techniques, the real-time PPG and pulse data are analyzed to achieve noninvasive physiological signal measurement. By considering both the cardiovascular pressure output and venous return systems, the model aims to accurately simulate peripheral blood circulation, enabling the creation of a hemodynamic model in physiological data and integrating the relationship between PPG waveforms and pulse signals. The hemodynamic model is established by dividing the human body into four major systems: the heart, the large arteries and medium-sized arteries, the arterioles and capillaries, and the venous system. The real-time PPG and pulse data are collected from the subject under normal conditions and then integrated with the hemodynamic model to generate output data and further refined through systematic computational modeling, resulting in a personalized hemodynamic model corresponding to the individual's PPG and pulse characteristics. By evaluating the results from the personalized node models under normal conditions, real-time, accurate, and effective results between the personalized model outputs and pulse waveforms are achieved. This process allows for the adjustment of the hemodynamic model parameters, thereby leading to the development of an optimized, individualized hemodynamic model tailored to the specific physiological conditions of the person.