Cardiovascular Diseases Diagnosis Using an ECG Multi-Band Non-Linear Machine Learning Framework Analysis

Abstract

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.

Keywords: ECG signals; cardiovascular diseases; discrete wavelet transform; discrimination; machine learning models; non-linear analysis.

Figure 1

Workflow diagram.

Figure 2

Violin plots of binary group distributions with significant differences—individual feature power analysis for discrimination. (a) $VHD$vs.$M$; (b) $VHD$vs.$MI$; (c) $VHD$vs.$HC$; (d) $VHD$vs.$BBB$; (e) $M$vs.$BBB$; (f) $MI$vs.$HC$; (g) $MI$vs.$CardMyo$; (h) $MI$vs.$BBB$; (i) $MH$vs.$HC$; (j) $HC$vs.$CardMyo$; (k) $HC$vs.$BBB$; (l) $Dis$vs.$BBB$.

Figure 2

Violin plots of binary group distributions with significant differences—individual feature power analysis for discrimination. (a) $VHD$vs.$M$; (b) $VHD$vs.$MI$; (c) $VHD$vs.$HC$; (d) $VHD$vs.$BBB$; (e) $M$vs.$BBB$; (f) $MI$vs.$HC$; (g) $MI$vs.$CardMyo$; (h) $MI$vs.$BBB$; (i) $MH$vs.$HC$; (j) $HC$vs.$CardMyo$; (k) $HC$vs.$BBB$; (l) $Dis$vs.$BBB$.

Figure 3

Heatmap classification report regarding combined feature discriminant power analysis—the best $Accuracy$, $Recall$, $Precision$, F1-$Score$, $AUC$, $Kappa$, $MCC$, $CSI$, and $Gmean$ results for each comparison group plus the information of lead and ML classifier applied for signal analysis.

Figure 4

Direct comparison using $Accuracy$ between individual and combined feature power analyses for binary groups’ discrimination performed by ML models.