Non-invasive and Automatic Identification of Diabetes Using ECG Signals

Abstract

Diabetes Mellitus is a chronic medical condition in which the body is unable to properly regulate the amount of glucose (a type of sugar) in the blood. It can cause serious consequences like heart disease, nerve damage, and kidney illness. Diabetes causes cardiac autonomic neuropathy, which affects the pattern of electrocardiogram (ECG) signals. ECG measures electrical activity of the hearts. In this paper, the features extraction method is proposed for the classification of diabetic ECG and normal ECG signals. Ten features, namely, log energy, threshold, Shannon, sure entropy, root mean square value, kurtosis, skewness, maximum value, energy, and variance are extracted from the single-lead ECG signal. Fisher-score has been employed for features ranking methods the ranked features are used as input to the classifiers namely medium tree, coarse Tree, linear discriminant, quadratic discriminant, and Gaussian naive Bayes, classifiers. The five ranked features using medium tree classifier has produced an accuracy of 87.19%. The analysis of performance measurement shows the effectiveness of the proposed method in the classification of diabetic and non-diabetic ECG signals.