一个联合的CNN-Bi-LSTM-变压器架构与SHAP解释,用于从12导电心电图中检测多标签心律失常
Mohammed T Al-Bairmani1,2, Mohammadreza Yazdchi3, Fahimeh Nasimi4
1Medical instrumentation Techniques Engineering Department, College of Engineering Technologies, Al-Mustaqbal University, Hillah 51001, Babil, Iraq.
Scientific reports
|February 26, 2026
概括
这项研究引入了一种先进的AI模型,用于精确的心电图 (ECG) 心律失常分类. 可解释的深度学习系统提高了临界心脏病的诊断速度和可靠性.
科学领域:
- 心血管诊断心血管诊断服务
- 人工智能在医学中的应用
- 深度学习用于信号处理.
背景情况:
- 对12导电心电图 (ECG) 用于心律不整的分类进行手动解释是耗时且不一致的.
- 现有的自动化人工智能模型往往缺乏复杂性,数据多样性和强大的验证.
- 需要可解释的AI解决方案来提高对心电图分析的准确性和临床信心.
研究的目的:
- 开发一种新的深度学习 (DL) 架构,用于准确和可解释的ECG心律失常分类.
- 结合卷积神经网络 (CNN),双向长期短期记忆 (Bi-LSTM) 和变压器层进行全面的心电图样式提取.
- 通过可解释AI (XAI) 方法确保临床透明度并支持诊断推理.
主要方法:
- 设计了一个混合DL模型,集成CNN,Bi-LSTM和变压器层.
- 该模型在大规模的PhysioNet/Computing in Cardiology Challenge 2020数据集 (超过43,000个心电图,27个心律失常类) 上进行了训练和验证.
- 为了逐个案例的解释性,纳入了SHAPley添加式解释 (SHAP),将预测与心电图特征联系起来.
主要成果:
- 该模型实现了高精度,宏观F1得分,以及超过[公式:参见文本]的ROC曲线下面面积 (AUC) 对于像心室早跳 (VPB) 和心房动 (AF) 这样的关键心律不整.
- SHAP分析提供了生理学上相关的解释,将预测与心脏病学家的诊断推理结合起来.
- 该模型以小的足迹 ([公式:见文本]) 和低的推理延迟 ([公式:见文本]) 证明了计算效率.
结论:
- 拟议的DL框架为自动ECG心律失常分类提供了一个强大,可扩展和可解释的解决方案.
- 它实现了高诊断性能,解决了阶级不平衡,并提供了人类层面的可解释性.
- 这项工作推进了可解释AI在心血管诊断中的使用,使其在实时监测和远程医疗中的潜在部署成为可能.
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