Key result
Random Forest model classifies ECG heartbeats with ~97% accuracy, boosting detection of rare beats.
Why the study?
Building a reliable ECG heartbeat classification model with improved prediction of uncommon fusion and unknown heartbeat classes is essential for early arrhythmia detection.
A Random Forest machine learning model using synthetic minority oversampling and combined datasets achieved 97% accuracy in classifying five types of ECG heartbeats, significantly improving recall for rare fusion and unknown beats.
Building an electrocardiogram (ECG) heartbeat classification model is essential for early arrhythmia detection. This research aims to build a reliable model that can classify heartbeats into five heartbeat types: normal beat (N), supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), fusion beat (F), and unknown beat (Q), with a focus on enhancing the predictions of the uncommon Q and F heartbeats. The base dataset used is the MIT-BIH SupraVentricular Database, which was used to train and compare the performance of five machine learning models: logistic regression, Random Forest (RF), K-nearest neighbor, linear support vector machine, and linear discriminant analysis. In addition to using the synthetic minority oversampling technique, data extracted from multiple databases for the F and Q classes were combined with the original base dataset. These methods resulted in significant improvement in the recall for the rare F and Q classes when compared to the literature. The RF algorithm produced the best performance with an accuracy of 97% and recall values equal to 97%, 93%, 95%, 95%, and 30% for N, SVEB, VEB, F, and Q, respectively.
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Al‐Mousa et al. (2023) studied Arrhythmia. Random Forest machine learning model vs. Logistic regression, K-nearest neighbor, linear support vector machine, linear discriminant analysis was evaluated on Classification accuracy. A Random Forest machine learning model classified ECG heartbeats with 97% overall accuracy and improved recall for rare fusion (95%) and unknown (30%) heartbeat classes.
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