A METHOD FOR DETECTING CORONARY ARTERY STENOSIS BASED ON ECG SIGNALS

Abstract

Coronary heart disease (CHD) is a typical cardiovascular disease whose occurrence and development is a long process. Timely and accurate diagnosis of patients with varying degrees of coronary artery stenosis (VDCAS) is conducive to accurate treatment and prognosis assessment. This study aims to correctly classify VDCAS patients by utilizing multi-domain features fusion of single-lead 5-min ECG signals and machine learning methods, so as to provide reference for doctors to judge the CHD development process. ECG signals were collected from 206 subjects with CHD, mild CHD, thoracalgia and normal coronary angiograms (TNCA), and healthy. Then, the time, frequency, time–frequency, and nonlinear domain features of ECG signals were extracted to establish a multi-domain feature set. To get the optimum subset of features, the recursive feature elimination (RFE) and information gain (IG) were selected. Subsequently, eXtreme Gradient Boosting (XGBoost) and random forest (RF) were adopted for classification. Results indicated that RFE combined with XGBoost was significantly effective in classifying VDCAS patients. When the four categories of subjects (CHD, mild CHD, TNCA, and healthy) were classified, the average accuracy, sensitivity, specificity, and F1-score of the proposed method were 91.74%, 89.39%, 96.80%, and 90.09%, respectively. Besides, three categories of subjects (no stenosis, luminal narrowing [Formula: see text] 50%, and luminal narrowing [Formula: see text] 50%) and two categories of subjects (CHD and healthy) were also analyzed, and the average accuracy was 91.27% and 98.46%, respectively. The results suggest that the proposed method can provide reference for doctors to judge VDCAS patients.