基于深度残留收缩网络和LSTM的ECG分类网络
Zhanhang Qiu1, Suigu Tang2, Huazhu Liu1,2
1School of Computer Science and Technology, Dongguan University of Technology, Dongguan, China.
Computer methods in biomechanics and biomedical engineering
|September 7, 2025
概括
这项研究介绍了CDLR-Net,这是一种用于精确心电图 (ECG) 分析的新型深度学习模型. 通过将RR间隔数据与心电图信号相结合,可显著改善心跳过早的检测.
科学领域:
- 心脏病学
- 生物医学工程
- 医疗保健中的人工智能
背景情况:
- 传统的心电图 (ECG) 分类网络通常仅依赖于MLII领先信号.
- 这种方法的准确性受到限制,特别是对于心电图中的微妙特征,如心跳过早.
研究的目的:
- 开发一个先进的心电图分类网络,CDLR-Net,提高心跳过早的准确性.
- 整合MLII心电图信号和RR间隔功能以提高诊断性能.
主要方法:
- 一个新的CDLR-Net结合了深度残留收缩网络 (DRSN) 和长期短期记忆 (LSTM).
- 用于ECG信号的波形分解.
- 在R波局部化后提取各种RR区间特征 (RR前,RR后,局部-10平均RR,整体平均RR).
主要成果:
- 在MIT-BIH数据库中实现了97%的准确性.
- 在MIT-BIH数据库中达到99%的准确性.
- 通过整合RR间隔信息,显著改善了心电图分类的准确性.
结论:
- 拟议的CDLR-Net有效地提高了心电图分类的准确性,
- 整合RR间隔特征与MLII心电图信号是可靠的心脏分析的有希望的策略.
- 在自动化心电图解释方面,CDLR-Net具有很高的临床应用潜力.
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