An ECG-Based Model for Left Ventricular Hypertrophy Detection: A Machine Learning Approach

Figures

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  Fig. 1.. Morphological features extraction: Interval width, negative % of the signal, amplitude of the fiducial points and slopes of the waves.

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  Fig. 2.. Methodological steps separated in training-test (top) and validation phase (bottom). The hundreds of training models are gathered to form a final ensemble model.

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  Fig. 3.. Mean area under the curve (AUC) during feature selection process, reorder by number of features. The three ML models are presented: logistic regression (LogReg), random forest (RF) and support vector machine (SVM).

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  Fig. 4.. Mean ROC curves of the 3 algorithms on the external validation database: Logistic Regression (LogReg), Random Forest (RF) and Support Vector Machine (SVM) for the selected features up to 30 features and to the reduced to 5 features: LogReg5, RF5, SVM5, with the point of their equivalent ensemble method. The best 5 clinical criteria: Siegel [40], Mc Phie [38], Sokolow-Lyon [28], Gant2 [36], Romhilt1 [31], and their combination are also plotted, on the external validation database.

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  Fig. 5.. Confusion matrix of the ensemble models: (a) SVM, (b) RF, (c) SVM5, and (d) RF5 on the external validation database.