集成的便携式心电图监测系统,具有CNN分类,用于早期检测心律失常
Aayush Panwar1, Modigari Narendra2, Arnav Arya1
1School of Electronics Engineering, Vellore Institute of Technology, Chennai, India.
Frontiers in digital health
|April 2, 2025
概括
本研究介绍了一种使用卷积神经网络 (CNN) 进行准确心律失常检测的便携式心电图系统. 该系统实现了高精度,为实时心脏监测提供了具有成本效益的解决方案.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 心脏病学 心脏病学
背景情况:
- 电心电图 (ECG) 对于诊断心脏病至关重要.
- 便携式监测解决方案对于及时检测心脏事件越来越重要.
- 对心脏电活动的实时分析对于有效的干预至关重要.
研究的目的:
- 开发一个便携式的心电图监测系统,用于准确的心脏异常分类.
- 实施卷积神经网络 (CNN) 来实时检测心律失常.
- 用现有基准来评估系统的性能.
主要方法:
- 使用了一台Arduino Nano微控制器和AD8232心电图传感器来获取信号.
- 在MIT-BIH心律失常数据集上训练的预处理的ECG数据和应用的CNN模型.
- 进行了二进制和多类分类,以区分正常和异常的心律.
主要成果:
- 实现了高分类准确度:98.35%的二进制分类和99.3%的多类分类.
- 与最先进的方法相比,表现出卓越的性能.
- 突出了系统的成本效益和实时分类能力.
结论:
- 便携式心电图系统为早期心律失常检测提供了可靠且具有成本效益的解决方案.
- 在临床和远程环境中,CNN集成确保了准确的心脏异常分类.
- 该系统具有显著的潜力,可以通过实时监测和早期诊断来改善心血管健康管理.
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