[ResLSTM-TemporalSE:为多导线心电图信号的自动分类模型]
1School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Nan fang yi ke da xue xue bao = Journal of Southern Medical University
|December 22, 2025
概括
一种新的深度学习模型ResLSTM-TemporalSE显著提高了12导电心电图 (ECG) 分类的准确性. 这种高效的框架整合了剩余连接和时间注意力,以便在临床环境中进行可靠的自动化心电图分析.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 心脏病学 心脏病学
背景情况:
- 12导电心电图 (ECG) 信号的自动分类对于诊断心脏疾病至关重要.
- 现有的深度学习模型在有效地捕捉ECG数据中的复杂时间依赖性和层次特征方面面临挑战.
研究的目的:
- 开发一个高效的深度学习模型,ResLSTM-TemporalSE,以提高12导电心电图信号的分类准确性.
- 整合残留学习和时间注意力机制,以改善从心电图数据中提取特征.
主要方法:
- 设计了一个新的ResLSTM-TemporalSE网络架构,结合了多层剩余长短期内存 (ResLSTM) 和跳过连接.
- 在Squeeze-and-Excitation (SE) 模块中集成了一个时间注意力机制,以捕捉长期的时间依赖.
- 该模型在公共CPSC2018数据集和私人临床数据集上得到了验证.
主要成果:
- 在CPSC2018测试组中实现了99.70%的分类准确度 (精度:0.9966,回忆:0.9370,F1分数:0.9653).
- 在私人临床数据集上达到82.77%的准确性 (精度:0.6811,回忆:0.8961,F1分数:0.7723).
- 废弃性研究证实了残留连接和时间注意力对性能的显著贡献.
结论:
- 该ResLSTM-TemporalSE模型有效地整合了时空特征,用于优越的ECG信号分类.
- 在基准和现实世界的临床数据集上表现出强大的概括能力.
- 为自动化心电图分析提供了一个强大的框架,具有显著的临床应用潜力.
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