Coronary Health Index (CHI) as A Determinant for Arterial Stenosis, Derived Using PPG and ECG Signals

Abstract

Cardiovascular disease (CVD) patients were targeted from cardiology department in this study to segregate who had stenosis and also identify the principal diseased coronary artery using PPG and ECG signals. After pre-processing these signals, dicrotic notch region of PPG and S-T segment of ECG, within each cardiac cycle was extracted as templates. A new fused segment was generated from two templates by a proposed algorithm. Utilizing statistics on three templates we defined the term Coronary Health Index (CHI) to evaluate the status of coronary arteries. Setting CHI thresholding values, healthy and stenosed artery were differentiated. Using CHI values from patients with stenosis, the classification of arteries (LAD, RCA, and LCx) was performed using Graph Attentive Convolution Network. Among 408 CVD patients 256 had occlusion in either LAD or RCA or LCx. Binary classification among presence and absence of stenosis was carried out with 0.92 accuracy, 0.91 recall, 0.91 precision, 0.90 specificity, and 0.92 F-score. Identification of stenosed artery was measured with Kappa score (0.89) and Youden's J statistic value (0.84). AUC(0.93) and AP(0.92) values from ROC and PRC curves, respectively. This derived CHI could be able to study stenosis in non-invasive, easy and cost-effective manner.