1. Discrimination of Patients with Varying Degrees of Coronary Artery Stenosis by ECG and PCG Signals Based on Entropy
- Author
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Xinpei Wang, Huan Zhang, Yuanyuan Liu, Yuanyang Li, Changchun Liu, Tongtong Liu, Huiwen Dong, Jikuo Wang, and Yu Jiao
- Subjects
medicine.medical_specialty ,Heartbeat ,Science ,QC1-999 ,phonocardiogram ,0206 medical engineering ,General Physics and Astronomy ,Feature selection ,02 engineering and technology ,electrocardiogram ,030204 cardiovascular system & hematology ,Astrophysics ,QT interval ,Article ,03 medical and health sciences ,0302 clinical medicine ,coupling analysis ,Internal medicine ,medicine ,coronary heart disease ,Entropy (energy dispersal) ,Phonocardiogram ,business.industry ,Physics ,cross fuzzy entropy ,Mutual information ,020601 biomedical engineering ,QB460-466 ,Sample entropy ,joint distribution entropy ,Feature (computer vision) ,cross sample entropy ,Cardiology ,business - Abstract
Coronary heart disease (CHD) is the leading cause of cardiovascular death. This study aimed to propose an effective method for mining cardiac mechano-electric coupling information and to evaluate its ability to distinguish patients with varying degrees of coronary artery stenosis (VDCAS). Five minutes of electrocardiogram and phonocardiogram signals was collected synchronously from 191 VDCAS patients to construct heartbeat interval (RRI)–systolic time interval (STI), RRI–diastolic time interval (DTI), HR-corrected QT interval (QTcI)–STI, QTcI–DTI, Tpeak–Tend interval (TpeI)–STI, TpeI–DTI, Tpe/QT interval (Tpe/QTI)–STI, and Tpe/QTI–DTI series. Then, the cross sample entropy (XSampEn), cross fuzzy entropy (XFuzzyEn), joint distribution entropy (JDistEn), magnitude-squared coherence function, cross power spectral density, and mutual information were applied to evaluate the coupling of the series. Subsequently, support vector machine recursive feature elimination and XGBoost were utilized for feature selection and classification, respectively. Results showed that the joint analysis of XSampEn, XFuzzyEn, and JDistEn had the best ability to distinguish patients with VDCAS. The classification accuracy of severe CHD—mild-to-moderate CHD group, severe CHD—chest pain and normal coronary angiography (CPNCA) group, and mild-to-moderate CHD—CPNCA group were 0.8043, 0.7659, and 0.7500, respectively. The study indicates that the joint analysis of XSampEn, XFuzzyEn, and JDistEn can effectively capture the cardiac mechano-electric coupling information of patients with VDCAS, which can provide valuable information for clinicians to diagnose CHD.
- Published
- 2021
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