Key result
Grad-CAM deep learning models classify five distinct ECG heartbeat categories in MIT-BIH data.
Why the study?
Manual analysis of ECG signals to identify abnormal heart rhythms is challenging, especially when large amounts of data are involved, motivating the exploration of machine learning and deep learning models for both performance and interpretability.
Do deep learning models (CNN, GRU, hybrid CNN-RNN, and attention-based architectures) with class balancing and Grad-CAM improve ECG heartbeat classification and interpretability compared to traditional machine learning algorithms in the MIT-BIH heartbeat dataset?
Do deep learning models (CNN, GRU, hybrid CNN-RNN, and attention-based architectures) with class balancing and Grad-CAM improve ECG heartbeat classification and interpretability compared to traditional machine learning algorithms in the MIT-BIH heartbeat dataset?
This computational study compares various machine learning and deep learning architectures alongside class-balancing techniques and Grad-CAM to optimize both accuracy and interpretability in automated ECG arrhythmia classification.
Answers are grounded in this paper and cite their sources.
Class balancing and visual explanations may enhance ECG arrhythmia models; leaves open prospective clinical validation before deployment.
Electrocardiogram (ECG) signals provide useful information about the functioning of the heart and help identify abnormal heart rhythms. While these signals are commonly used in cardiac monitoring, analysing them manually can be challenging, especially when large amounts of data are involved. This project explores how machine learning and deep learning can be used to classify ECG heartbeats and distinguish between different heartbeat patterns. The work uses the MIT-BIH heartbeat dataset, which contains five categories of heartbeats: Normal, Supraventricular, Ventricu lar, Fusion and Unknown. Different traditional machine learning algorithms and deep learning models are explored. These include CNN, GRU, hybrid CNN-RNN and attention-based architectures. Since the dataset contains an unequal distribution of heartbeat classes, techniques such as SMOTE, data augmentation and class weighting are also considered. Grad-CAM is used alongside the deep learning models to highlight the parts of an ECG signal that influence their predictions. Through these approaches, the project examines the differ ences between various classification models, particularly in their ability to recognise less frequent heartbeat categories. It also looks at how visual explanations can help in understanding the predictions made by deep learning models. The overall aim is to explore different approaches to ECG heartbeat classification while considering both model performance and interpretability.
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Pahuja et al. (2026) studied Arrhythmia. Deep learning models (CNN, GRU, hybrid CNN-RNN, and attention-based architectures) with Grad-CAM vs. Traditional machine learning algorithms was evaluated on ECG heartbeat classification across five categories (Normal, Supraventricular, Ventricular, Fusion, and Unknown). Machine learning and deep learning models with Grad-CAM were explored to classify 5 ECG heartbeat categories in the MIT-BIH dataset, though no quantitative results were reported in the abstract.
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