An explainable deep learning framework for trustworthy arrhythmia detection from ECG signals

Cardiovascular diseases (CVDs) constitute a foremost global health challenge, with cardiac arrhythmias significantly increasing both mortality and morbidity. Early and precise detection of these arrhythmias from Electrocardiogram (ECG) signals is paramount but inherently complex due to the vast volume, diverse characteristics and variability of ECG data. While Deep Learning (DL) models offer transformative potential for automated ECG analysis, their widespread clinical adoption is hindered by issues such as susceptibility to overfitting, high computational demands and a notable lack of interpretability, resulting in black-box systems. This paper presents an explainable DL framework for accurate and reliable arrhythmia detection. Our innovative approach integrates advanced DL architectures, specifically Convolutional Neural Network (CNN) and Dense Neural Network (DNN), within a sophisticated multi-stage pipeline. This pipeline encompasses meticulous data preparation, state-of-the-art signal preprocessing and robust multi-strategy data balancing techniques, including ADASYN, SMOTE, SMOTETomek and Random Over-Sampling (ROS), to maximize model performance and generalization. Crucially, the framework incorporates Explainable Artificial Intelligence (XAI) methodologies-namely SHAP, LIME and Feature Importance Analysis (FIA) to provide transparent insights into the model's decision-making process. Rigorous evaluation on benchmark ECG datasets such as MITDB, PTBDB and NSTDB, demonstrates superior classification accuracy, with our ROS+CNN model achieving 99.74%, 99.43% and 99.98%, respectively. The embedded XAI components offer actionable interpretability, fostering clinical trust and paving the way for more reliable and impactful AI-driven cardiovascular diagnostics.