Explainable Parallel CNN-LSTM Model for Differentiating Ventricular Tachycardia from Supraventricular Tachycardia with Aberrancy in 12-Lead ECGs

2025-07-14

AI Summary

Distinguishing ventricular tachycardia (VT) from supraventricular tachycardia with aberrant conduction (SVT-A) in wide-QRS-complex tachycardias remains a clinically challenging task due to high misdiagnosis risk. To address this, we propose a lightweight parallel CNN-LSTM model for end-to-end classification of 12-lead ECG signals: one branch employs 1D-CNNs to extract local morphological features, while the other utilizes LSTMs to capture temporal dynamics; features from both branches are fused in parallel. Furthermore, SHAP analysis is integrated to provide both global and local interpretability, enhancing clinical trustworthiness without compromising diagnostic accuracy. Evaluated on real-world data from 35 patients, the model achieves 95.63% accuracy, 95.10% sensitivity, and 96.06% specificity—outperforming existing methods. With minimal parameter count and efficient inference, it demonstrates strong potential for clinical deployment.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Interpretability, Explainability, and Transparency
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Large-scale security measurements

Abstract

Background and Objective: Differentiating wide complex tachycardia (WCT) is clinically critical yet challenging due to morphological similarities in electrocardiogram (ECG) signals between life-threatening ventricular tachycardia (VT) and supraventricular tachycardia with aberrancy (SVT-A). Misdiagnosis carries fatal risks. We propose a computationally efficient deep learning solution to improve diagnostic accuracy and provide model interpretability for clinical deployment. Methods: A novel lightweight parallel deep architecture is introduced. Each pipeline processes individual ECG leads using two 1D-CNN blocks to extract local features. Feature maps are concatenated across leads, followed by LSTM layers to capture temporal dependencies. Final classification employs fully connected layers. Explainability is achieved via Shapley Additive Explanations (SHAP) for local/global interpretation. The model was evaluated on a 35-subject ECG database using standard performance metrics. Results: The model achieved $95.63%$ accuracy ($95%$ CI: $93.07-98.19%$), with sensitivity=$95.10%$, specificity=$96.06%$, and F1-score=$95.12%$. It outperformed state-of-the-art methods in both accuracy and computational efficiency, requiring minimal CNN blocks per pipeline. SHAP analysis demonstrated clinically interpretable feature contributions. Conclusions: Our end-to-end framework delivers high-precision WCT classification with minimal computational overhead. The integration of SHAP enhances clinical trust by elucidating decision logic, supporting rapid, informed diagnosis. This approach shows significant promise for real-world ECG analysis tools.

Problem

• Differentiate VT from SVT-A in 12-lead ECGs
• Improve diagnostic accuracy with deep learning
• Provide interpretability for clinical deployment

Innovation

• Parallel CNN-LSTM model for ECG analysis
• SHAP for model interpretability
• Lightweight architecture with high accuracy