arXiv 2507.141962025-07-14eess.SPcs.AIcs.LG

用于区分12导联心电图中室性心动过速与伴差异性传导的室上性心动过速的可解释并行CNN-LSTM模型

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

Cardiac Rehabilitation Research Center（心脏康复研究中心）；Cardiovascular Research Institute（心血管研究所）；Department of Cardiology（心内科部）；School of Medicine（医学院）；University of Isfahan（伊斯法罕大学）；Department of Computer Science（计算机科学系）；Faculty of Engineering（工程学院）；Department of Electrical, Computer and Biomedical Engineering（电气、计算机与生物医学工程系）；Toronto Metropolitan University（多伦多 Metropolitan 大学）；Faculty of Electrical Engineering（电气工程学院）；K.N. Toosi University of Technology（K.N. Toosi 技术大学）

Zahra Teimouri-Jervekani, Fahimeh Nasimi, Mohammadreza Yazdchi, Ghazal MogharehZadeh, Javad Tezerji, Farzan Niknejad Mazandarani, Maryam Mohebbi

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AI总结：

本文提出一种轻量级并行CNN-LSTM深度学习模型，结合SHAP可解释性分析，用于区分12导联心电图中的室性心动过速与伴差异性传导的室上性心动过速，在35名受试者数据上取得95.63%准确率并优于现有方法。

摘要：

背景与目的：宽QRS波心动过速（WCT）的鉴别在临床上至关重要，但由于危及生命的室性心动过速（VT）与伴差异性传导的室上性心动过速（SVT-A）在心电图（ECG）信号中的形态学相似性，这一任务仍具有挑战性。误诊会带来致命风险。我们提出一种计算高效的深度学习解决方案，以提高诊断准确性并为临床部署提供模型可解释性。方法：本文引入一种新颖的轻量级并行深度架构。每条流水线使用两个1D-CNN模块处理单个心电图导联以提取局部特征。各导联的特征图被拼接后，经LSTM层捕获时间依赖性。最终分类采用全连接层。可解释性通过Shapley加性解释（SHAP）实现局部/全局解释。该模型在一个包含35名受试者的心电图数据库上使用标准性能指标进行评估。结果：该模型达到95.63%的准确率（95%置信区间：93.07-98.19%），敏感性为95.10%，特异性为96.06%，F1分数为95.12%。它在准确率和计算效率上均优于现有最优方法，每条流水线仅需最少的CNN模块。SHAP分析展示了具有临床可解释性的特征贡献。结论：我们的端到端框架以最小的计算开销实现了高精度的WCT分类。SHAP的集成通过阐明决策逻辑增强了临床信任，支持快速、有依据的诊断。该方法在现实世界心电图分析工具中展现出显著前景。

英文摘要：

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.

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