在可穿戴式心电图监测中强大的节律失常检测的通用化混合波形深度学习架构
Ukesh Thapa1, Bipun Man Pati1, Attaphongse Taparugssanagorn2
1Advanced College of Engineering and Management, Tribhuvan University, Kathmandu 44600, Nepal.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
本研究引入了对心电图 (ECG) 节律分类的深度学习框架,将时间频率分析与手工制作的功能相结合. 该框架实现了可穿戴设备实时监控的高精度和效率.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 电心电图 (ECG) 分析对于诊断心脏病至关重要.
- 由于信号变化和噪声,准确和高效的心电图节奏分类具有挑战性.
- 深度学习为自动ECG解释提供了有前途的途径.
研究的目的:
- 开发和评估用于ECG节律分类的渐进深度学习框架.
- 研究将时间频率表示 (刻度图) 与手工制作的特征相结合的有效性.
- 评估用于ECG分析的各种深度学习架构的性能和效率.
主要方法:
- 电脑心电图信号被转化为头图,并由视觉转换器 (ViT) 和其他架构处理.
- 尺度图与散射和统计特征融合,以提高稳定性.
- 应用主要组件分析 (PCA) 来减少特征维度.
- 训练时间的增加被用来解决阶级不平衡.
主要成果:
- 视觉变压器 (ViT) 使用基于纯图像的心电图分析实现了高精度 (0.8590).
- 具有融合特征的FusionViT产生了最好的性能 (精度=0.8623,F1得分=0.8528).
- 融合ResNet-18提供了准确性和推断效率 (每样本0.016秒) 之间的平衡.
- 在保持竞争性性能的同时,PCA减少了功能维度.
结论:
- 拟议的框架证明了高准确性,稳定性和高效性,用于实际的心电图节奏分类.
- 视觉和统计特征的组合,随着可选的PCA减少,是有效的.
- 该框架适用于边缘设备 (如可穿戴设备和移动健康应用程序) 的实时监控.
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