Advanced International Journal for Research

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A Comparative Deep Learning Framework for ECG Signal Classification with Explainable AI Insights

Author(s) Mr. Akash Tripathi
Country India
Abstract Signals from electrocardiograms (ECGs) are an essential diagnostic tool for identifying cardiovascular diseases, which continue to be the leading cause of death worldwide [2], [3]. However, the development of automated analyt-ical frameworks is required because traditional manual interpretation of ECG recordings is labour-intensive, time-consuming, and prone to human error [9]. With a focus on differentiating between normal and abnormal heart-beats, this study suggests a sophisticated and comprehensible deep learning-based methodology for the classification of ECG signals. To improve signal fidelity and reduce noise artefacts, the suggested framework uses advanced signal preprocessing methods like bandpass filtering, notch filtering, and baseline wander elimination. Stable model training is made possible by sub-sequent segmentation and normalization processes, which guarantee con-sistent data representation [14]. Several resampling techniques, including Random Oversampling, SMOTE, ADASYN, and SMOTE-Tomek, were sys-tematically assessed to address the common problem of class imbalance; SMOTE was found to be the most effective method [35].
Furthermore, by employing deep learning architectures to extract intricate temporal and morphological features from ECG signals, the framework enhances classification robustness. Explainable AI ensures that model predictions are reliable and clinically significant while also improving interpretability.
Keywords Arrhythmia Detection, Electrocardiogram (ECG), ANN, DNN, Deep Learning, Convolutional Neural Networks (CNN), Explainable Artificial Intelligence (XAI), Data Balancing (ROS).
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 2, March-April 2026
Published On 2026-04-24
DOI https://doi.org/10.63363/aijfr.2026.v07i02.4995
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