An ECG multi-band non-linear machine learning framework achieved discrimination accuracies between 73% and 100%, recall between 68% and 100%, and AUC between 0.42 and 1 for distinguishing seven CVDs.
Does a machine learning framework using non-linear features from ECG signals accurately distinguish between different cardiovascular diseases?
A machine learning framework using non-linear features from ECG signals demonstrated high accuracy in distinguishing between seven different cardiovascular diseases and healthy controls.
BACKGROUND: cardiovascular diseases (CVDs), which encompass heart and blood vessel issues, stand as the leading cause of global mortality for many people. METHODS: the present study intends to perform discrimination between seven well-known CVDs (bundle branch block, cardiomyopathy, myocarditis, myocardial hypertrophy, myocardial infarction, valvular heart disease, and dysrhythmia) and one healthy control group, respectively, by feeding a set of machine learning (ML) models with 10 non-linear features extracted every 1 s from electrocardiography (ECG) lead signals of a well-known ECG database (PTB diagnostic ECG database) using multi-band analysis performed by discrete wavelet transform (DWT). The ML models were trained and tested using a leave-one-out cross-validation approach, assessing the individual and combined capabilities of features, per each lead or combined, to distinguish between pairs of study groups and for conducting a comprehensive all vs. all analysis. RESULTS: the Accuracy discrimination results ranged between 73% and 100%, the Recall between 68% and 100%, and the AUC between 0.42 and 1. CONCLUSIONS: the results suggest that our method is a good tool for distinguishing CVDs, offering significant advantages over other studies that used the same dataset, including a multi-class comparison group (all vs. all), a wider range of binary comparisons, and the use of classical non-linear analysis under ECG multi-band analysis performed by DWT.
Ribeiro et al. (Sun,) conducted a other in Cardiovascular diseases. ECG multi-band non-linear machine learning framework vs. Healthy control group and other CVDs was evaluated on Discrimination between seven CVDs and healthy controls. An ECG multi-band non-linear machine learning framework achieved discrimination accuracies between 73% and 100%, recall between 68% and 100%, and AUC between 0.42 and 1 for distinguishing seven CVDs.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: