Discrimination of Patients with Varying Degrees of Coronary Artery Stenosis by ECG and PCG Signals Based on Entropy

Autor:	Xinpei Wang, Huan Zhang, Yuanyuan Liu, Yuanyang Li, Changchun Liu, Tongtong Liu, Huiwen Dong, Jikuo Wang, Yu Jiao
Rok vydání:	2021
Předmět:	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
Zdroj:	Entropy Volume 23 Issue 7 Entropy, Vol 23, Iss 823, p 823 (2021)
ISSN:	1099-4300
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::40be44a48f9b18c4b1f1b481f3f6e9d6 https://doi.org/10.3390/e23070823 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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