一种深度学习方法,以区分左与右的异常心室节律失常,起源于输出管道
Reza Talebzadeh1, Hossein Khosravi1, Majid Haghjoo2
1Faculty of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran.
Journal of medical signals and sensors
|October 31, 2025
概括
深度学习使用心电图数据准确地识别了来自心脏外流通道的异常心室节律失常 (VA) 的起源. 这种1D-CNN模型提供了一个有希望的,非侵入性的诊断工具,用于指导导导管切除治疗.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 异常心室节律失常 (VA) 是一种常见的心脏病,通常起源于复杂的外流通道 (OT).
- 正确诊断VA起源是具有挑战性的,因为解剖学复杂性和右和左OT起源之间的ECG特征相似.
- 错误诊断可能会使治疗决策复杂化,特别是在导管切除手术中.
研究的目的:
- 开发一种方法来检测源自外流通道的VA的原产地.
- 为了提高诊断准确度,引导导管切除治疗.
- 为了利用深度学习进行非侵入性VA诊断.
主要方法:
- 利用了334名患有特异性VA患者的公共数据集.
- 处理标准的12导心电图数据,将其转化为一维 (1D) 节拍流.
- 对比了1D深度学习模型的性能:LSTM,GRU和1D-CNN.
主要成果:
- 1D-CNN模型实现了最高的性能.
- 获得了93.4%的精度和0.9313.3的F1得分.
- 与其他1D DL模型相比,表现出卓越的性能.
结论:
- 深度学习,特别是1D-CNN,有效地检测VA源于流出通道.
- 这种DL方法超越了传统的计算机化心电图分析方法.
- 为指导治疗的未来临床应用提供了一个有希望的非侵入性诊断工具.
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