Hybrid BiLSTM-CNN Model with Attention and XAI (SHAP & LIME) for ECG Arrhythmia Classification Using PTB-XL
Abstract: Analysis of electrocardiograms (ECG) is vital to identify cardiological problems; however, traditional interpretation requires a lot of clinical knowledge and is influenced by inter-observer variability. In order to solve these problems, this research presents an interpretable deep learning framework that fuses Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM) networks, and an Attention mechanism for multi-label ECG classification. In addition to employing the PTB-XL dataset, the system commits to clinical trust by integrating explainability methods SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) to enhance transparency.The model reaches a macro F1-score of 0.369 and an ROC-AUC of 0.919, reflecting its high capability of simultaneous multi-class identification of different cardiac diseases. The SHAP Attention superposition helps in providing easy-to-understand explanations, which enable the doctor to see the time-related and shape-related factors of the ECG waveform.This paper focuses on the importance of combining accuracy with explanation in AI healthcare systems and provides a robust, transparent method for computer-assisted cardiac diagnosis.
Keywords: ECG Classification, SHAP, LIME, CNN-BiLSTM, Attention Mechanism, Deep Learning, PTB-XL Dataset, Multi-Label Classification, Biomedical Signal Processing
How to Cite?: Maryam Shadan, Adicherla Sai Chaithanya, Chikoti Vaishali, Syed Mohammed Musharraf, Mujtaba Gulam Muqeeth, "Hybrid BiLSTM-CNN Model with Attention and XAI (SHAP & LIME) for ECG Arrhythmia Classification Using PTB-XL", Volume 14 Issue 12, December 2025, International Journal of Science and Research (IJSR), Pages: 2242-2249, https://www.ijsr.net/getabstract.php?paperid=SR251226130813, DOI: https://dx.doi.org/10.21275/SR251226130813