Implementation of Trajectory Analysis System for Metabolic Syndrome Detection

Abstract

Metabolic syndrome is not only a major public health problem in the United States and other developed countries but also is associated with increased risk for diabetes and coronary heart disease (CHD). This study presents a easy method for metabolic syndrome detection by measuring the digital volume pulse through the finger photoplethysmography (PPG). The trajectory analysis (TA) has been adopted to produce Poincare plots with the PPG signals. The Poincare plots are two-dimensional graphical representations (scatter plots) of PPG signals. The standard deviation along the longitudinal axis (SD2) in the plots are larger for the subjects with metabolic syndrome than who are without metabolic syndrome. SD2 in the Poincare map of finger plethysmographic singnals is a good indicator to detect metabolic syndrome. In this study, we hope to develop a simple detecting indicator of metabolic syndrome, and find a simple way to judge metabolic syndrome through the finger pulse infrared sensor for the non-linear analysis of Poincaré plot. The indicator can remind the user to pay attention to their health, control of eating and living habits to reduce future serious chronic diseases at any time.