Monitoring of Peripheral Vascular Disease Using Neuro-Fuzzy Algorithm and Wireless Body Sensor Network

Abstract

Peripheral vascular disease (PVD) is a cardiovascular disease that mainly affects the arms and legs. Left untreated, PVD can result in heart complications and eventually death. As such, a novel method of non-invasively monitoring PVD has been devised. The developed system makes use of a wireless body sensor network that obtains electrocardiogram (ECG) and photoplethysmography (PPG) signals from the patient in real-time. The sensor readings are then converted to their equivalent blood pressure readings, which are then evaluated by neuro-fuzzy logic and the computation of the patient's Mean Arterial Pressure (MAP). Under the null hypothesis that there is no significant difference between the estimated and actual values for systolic and diastolic blood pressures, two-tailed t-tests were run on the two datasets under a 95% confidence interval and a critical t-test value of +/-1.985. The computed t-test values from the statistical treatment were -1.10195 and -1.4675. Since both values were below critical, the null hypothesis is accepted for both tests, which means that the fabricated prototype is a suitable alternative to commercial devices when it comes to helping monitor and diagnose PVD.