一个轻量级的1D卷积神经网络模型用于从心电图信号诊断心律不整
Beaudelaire Saha Tchinda1, Daniel Tchiotsop2
1URAIA, Fotso Victor University Institute of Technology, University of Dschang, P.O Box 134, Bandjoun, Cameroon. beaudelaire.saha@univ-dschang.org.
Physical and engineering sciences in medicine
|February 25, 2025
概括
我们开发了一个简单的1D卷积神经网络,用于使用心电图 (ECG) 数据诊断心律不整. 这种人工智能模型在公共数据集上实现了高精度和高性能,为自动心律分析提供了高效的解决方案.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 电心电图 (ECG) 对于诊断心脏病至关重要.
- 目前用于ECG分析的AI模型经常面临复杂性和性能方面的挑战.
- 需要高效准确的自动化系统来诊断心律失常.
研究的目的:
- 提出一个简单但有效的1D卷积神经网络 (CNN) 模型用于心律失常诊断.
- 解决ECG分析中现有的AI模型的复杂性和性能限制.
- 评估模型的性能和对公共数据集的概括能力.
主要方法:
- 设计了一种1D CNN模型,包括卷积层,最大聚合层,密度层,掉落层和平面层.
- 该模型应用于MIT-BIH数据库,用于对五个AAMI标准心跳节奏进行患者间分类.
- 使用PTB数据库来评估模型的概括能力.
主要成果:
- 在MIT-BIH数据库中,该模型的精度为0.9842,AUC为0.99,F1测量为0.9095.
- 对PTB数据集的评估显示了高性能,准确度为0.9924和AUC为1.
- 该模型显示出具有竞争力的性能,特别是在患者间分类中的不平衡数据方面.
结论:
- 提出的简单的1D CNN模型为自动心律失常诊断提供了一个有希望的解决方案.
- 该模型平衡了嵌入式系统的简单性与高诊断性能.
- 这种方法可以提高基于心电图的心脏病诊断的质量和效率.
相关概念视频
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