A Robust Framework for Automated Screening of Diabetic Patient Using ECG Signals

Abstract

Diabetes mellitus (DM) or diabetes is an incurable, chronic, and genetic link health problem that occurs due to the higher glucose level in the blood. Continuous monitoring and automatic screening of diabetic patients will significantly improve the quality of the medical management system. DM induces cardiovascular autonomic neuropathy, which alters the morphology of electrocardiogram (ECG) signals. Hence, in this experiment, a new single-lead ECG signal database of 86 subjects (35 diabetic and 51 normal) is recorded. For the automatic screening of DM, an intrinsic time-scale decomposition (ITD) and machine learning-based framework is developed. In the first stage, denoised recorded signals are segmented into fragments of 5-s and decomposed into rotational components using the ITD algorithm. In the second stage, four features, namely, Hjorth complexity, Shannon entropy, log energy entropy, and log energy, are extracted from the ITD components. In the third stage, the Kruskal–Wallis (K–W) test is applied to select the most distinguishable features and fed to a decision tree classifier (DTC) with three different kernel functions for the automatic detection of diabetic patients. The fine tree (FT) kernel function provides the highest classification accuracy (ACC) of 86.9%. The proposed framework is developed using a 10-fold validation strategy. The developed framework is patient-centered suitable for screening in resource-limited environments and ready to be tested with more databases.