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

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 823
Author(s):  
Huan Zhang ◽  
Xinpei Wang ◽  
Changchun Liu ◽  
Yuanyang Li ◽  
Yuanyuan Liu ◽  
...  
Keyword(s):  
Coronary Artery ◽  
Qt Interval ◽  
Coronary Artery Stenosis ◽  
Coherence Function ◽  
Artery Stenosis ◽  
Recursive Feature Elimination ◽  
Joint Analysis ◽  
Support Vector ◽  
Time Interval ◽  
Electric Coupling

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.

scholarly journals A METHOD FOR DETECTING CORONARY ARTERY STENOSIS BASED ON ECG SIGNALS

2021 ◽  
Vol 21 (01) ◽  
pp. 2150003
Author(s):  
HUAN ZHANG ◽  
XINPEI WANG ◽  
CHANGCHUN LIU ◽  
YUANYANG LI ◽  
YUANYUAN LIU ◽  
...  
Keyword(s):  
Coronary Artery ◽  
Coronary Artery Stenosis ◽  
Artery Stenosis ◽  
Recursive Feature Elimination ◽  
Gradient Boosting ◽  
Time Frequency ◽  
Ecg Signals ◽  
Luminal Narrowing ◽  
Average Accuracy ◽  
Extreme Gradient Boosting

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.

Two cases of unstable angina with proximal coronary artery stenosis diagnosed by transthoracic Doppler echocardiography

Choonpa Igaku ◽  
2008 ◽  
Vol 35 (4) ◽  
pp. 443-449 ◽  
Author(s):  
Yuko SUGIYAMA ◽  
Masayo SUZUKI ◽  
Keiichi HIRANO ◽  
Keijirou NAKAMURA ◽  
Mao TAKAHASHI ◽  
...  
Keyword(s):  
Coronary Artery ◽  
Unstable Angina ◽  
Doppler Echocardiography ◽  
Coronary Artery Stenosis ◽  
Artery Stenosis ◽  
Transthoracic Doppler Echocardiography ◽  
Proximal Coronary Artery

Relationship between Whole Blood Viscosity and Lesion Severity in Coronary Artery Disease

2021 ◽  
Author(s):  
Gökhan Ceyhun ◽  
Oğuzhan Birdal
Keyword(s):  
Coronary Artery ◽  
Shear Rate ◽  
Confidence Interval ◽  
Odds Ratio ◽  
Coronary Artery Stenosis ◽  
Artery Stenosis ◽  
Significant Stenosis ◽  
Whole Blood Viscosity ◽  
Significant Coronary Artery Stenosis ◽  
Coronary Syndrome

Abstract Objective This article investigates the relationship of fractional flow reserve (FFR) with whole blood viscosity (WBV) in patients who were diagnosed with chronic coronary syndrome and significant stenosis in the major coronary arteries and underwent the measurement of FFR. Material and Method In the FFR measurements performed to evaluate the severity of coronary artery stenosis, 160 patients were included in the study and divided into two groups as follows: 80 with significant stenosis and 80 with nonsignificant stenosis. WBVs at low shear rate (LSR) and high shear rate (HSR) were compared between the patients in the significant and nonsignificant coronary artery stenosis groups. Results In the group with FFR < 0.80 and significant coronary artery stenosis, WBV was significantly higher compared with the group with nonsignificant coronary artery stenosis in terms of both HSR (19.33 ± 0.84) and LSR (81.19 ± 14.20) (p < 0.001). In the multivariate logistic regression analysis, HSR and LSR were independent predictors of significant coronary artery stenosis (HSR: odds ratio: 1.67, 95% confidence interval: 1.17–2.64; LSR: odds ratio: 2.46, 95% confidence interval: 2.19–2.78). In the receiver operating characteristic (ROC) curve analysis, when the cutoff value of WBV at LSR was taken as 79.23, it had 58.42% sensitivity and 62.13% specificity for the prediction of significant coronary artery stenosis (area under the ROC curve: 0.628, p < 0.001). Conclusion WBV, an inexpensive biomarker that can be easily calculated prior to coronary angiography, was higher in patients with functionally severe coronary artery stenosis, and thus could be a useful marker in predicting the hemodynamic severity of coronary artery stenosis in patients with chronic coronary syndrome.

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