XAI-ECGNet: an explainable hybrid CNN-BiLSTM framework for multi-label cardiac diagnosis classification from 12-lead ECG signals using the PTB-XL dataset

Abstract Cardiovascular disorders cause approximately 17.9 million deaths annually, and the 12-lead electrocardiogram remains the primary non-invasive diagnostic tool, yet interpretable deep-learning ECG classifiers remain limited, restricting clinical adoption. We present XAI-ECGNet, which is based on 1D residual CNNs, squeeze-and-excitation (SE) channel attention on learned, lead-derived feature channels, and bidirectional LSTM (BiLSTM) temporal modelling. On PTB-XL (21,799 ECGs, 18,885 patients), XAI-ECGNet outperforms all of the top performing baselines in terms of macro-AUROC, which is 0.9337, within the range of the baselines’ bootstrap confidence intervals, and four of five diagnostic superclasses have AUROC above 0.90. Ablation results indicate that the full model is within five-seed variability (σ = 0.0023) of the CNN-only backbone (0.9333); per-class analysis reveals that SE attention is crucial for BiLSTM to offer benefit, as it recovers and surpasses CNN-only performance on four out of five classes after BiLSTM alone fails to do so. Three post-hoc methods (Grad-CAM, SHAP, Integrated Gradients) show the model learns clinically consistent representations without explicit clinical priors. We also report quantitative faithfulness evaluation, SHAP background-sample-size justification, and zero-shot cross-dataset validation on CPSC 2018, demonstrating genuine generalisation for NORM and CD, and interpretable label-taxonomy mismatch for STTC. XAI-ECGNet combines accurate multi-label ECG classification with a rigorously and quantitatively assessed explainability framework, but prospective cardiologist validation is required before any claim to clinical use can be made.