Diabetic diagnose test based on PPG signal and identification system

Abstract

In this paper, photoplethysmogram (PPG) signals from two classes consisting of healthy and diabetic subjects have been used to estimate the parameters of Auto-Regressive Moving Average (ARMA) models. The healthy class consists of 70 healthy and the diabetic classes of 70 diabetic patients. The estimated ARMA parameters have then been averaged for each class, leading to a unique representative model per class. The order of the ARMA model has been selected as to achieve the best classification. The resulting model produces a specificity of %91.4 and a sensitivity of, %100. The proposed technique may find applications in determining the diabetic state of a subject based on a non-invasive signal.