High-Resolution Body Surface Potential Mapping in Exercise Assessment of Ischemic Heart Disease

Predicting angiographic coronary artery disease using machine learning and high-frequency QRS

AIM

Exercise stress ECG is a common diagnostic test for stable coronary artery disease, but its sensitivity and specificity need to be further improved. In this paper, we construct a machine learning model for the prediction of angiographic coronary artery disease by HFQRS analysis of cycling exercise ECG.

METHODS AND RESULTS

This study prospectively included 140 inpatients and 59 healthy volunteers undergoing cycling exercise ECG. The CHD group (N=104) and non-CHD group (N=95) were determined by coronary angiography gold standard. Automated HF QRS analysis was performed by the blinded method. The coronary group was predominantly male, with a higher prevalence of age, BMI, hypertension, and diabetes than the non-coronary group ( P < 0.001 ), higher lipid levels in the coronary group ( P < 0.005 ), significantly longer QRS duration during exercise testing ( P < 0.005 ), more positive leads ( P < 0.001 ), and a greater proportion of significant changes in HFQRS ( P < 0.001 ). Age, Gender, Hypertension, Diabetes, and HF QRS Conclusions were screened by correlation analysis and multifactorial retrospective analysis to construct the machine learning models of the XGBoost Classifier, Logistic Regression, LightGBM Classifier, RandomForest Classifier, Artificial Neural Network and Support Vector Machine, respectively.

CONCLUSION

Male, elderly, with hypertension, diabetes mellitus, and positive exercise stress test HFQRS conclusions suggested a high risk of CHD. The best performance of the Logistic Regression model was compared, and a column line graph for assessing the risk of CHD was further developed and validated.

Body Surface Potential Mapping: Contemporary Applications and Future Perspectives

Body surface potential mapping (BSPM) is a noninvasive modality to assess cardiac bioelectric activity with a rich history of practical applications for both research and clinical investigation. BSPM provides comprehensive acquisition of bioelectric signals across the entire thorax, allowing for more complex and extensive analysis than the standard electrocardiogram (ECG). Despite its advantages, BSPM is not a common clinical tool. BSPM does, however, serve as a valuable research tool and as an input for other modes of analysis such as electrocardiographic imaging and, more recently, machine learning and artificial intelligence. In this report, we examine contemporary uses of BSPM, and provide an assessment of its future prospects in both clinical and research environments. We assess the state of the art of BSPM implementations and explore modern applications of advanced modeling and statistical analysis of BSPM data. We predict that BSPM will continue to be a valuable research tool, and will find clinical utility at the intersection of computational modeling approaches and artificial intelligence.

Optimal ECG Lead System for Exercise Assessment of Ischemic Heart Disease

The diagnostic value of an ECG exercise test in diagnosis of ischemic heart disease (IHD) is limited. We investigated whether it is possible to develop a method for diagnosis of IHD which uses a low number of optimal ECG leads and has a higher diagnostic efficiency than conventional exercise ECG. This study was carried out on 43 patients. The 67-lead high-resolution ECG was recorded at rest and during exercise. The diagnostic value of ST segment depression (ΔST60) and T-wave morphology change (δT) determined in optimized ECG lead configurations was higher than for the standard 12-lead ECG. The best results were obtained for δT determined from 6 ECG electrode locations where sensitivity and specificity were 70% and 69% whereas for the standard exercise ECG were 63% and 62%, respectively. The small number of ECG leads used allows for easy hardware implementation of the methods for use in clinical settings.