一种基于物联网的健康监测系统的心电图信号处理和心脏病预测方法,使用优化表观神经网络来进行健康监测
B Sushma1, P Chinniah2, P S Ramesh3
1Department of Information Technology, MLR Institute of Technology, Hyderabad, India.
Electromagnetic biology and medicine
|May 10, 2025
概括
这项研究引入了一种高效的基于心电图的系统,用于使用先进的信号处理和优化的神经网络来预测心脏病. 该框架实现了实时物联网健康监测的高精度.
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
- 信号处理 信号处理
背景情况:
- 心脏病越来越普遍,需要先进的监测解决方案.
- 现有的物联网健康系统需要提高早期检测的效率和准确性.
- 电心电图 (ECG) 信号对于诊断心脏病至关重要.
研究的目的:
- 开发一个高效的基于心电图的框架,用于心脏病预测.
- 提高物联网健康监测中的计算效率和分类准确性.
- 为实时心脏监测提供可扩展,高性能解决方案.
主要方法:
- 使用卷积式轻量级深度自动编码器维纳波器 (CLDAWF) 进行心电图信号预处理.
- 采用量化离散毛波变换 (QD-HWT) 来提取关键的心脏特征 (P波,QRS复合体,T波间隔).
- 优化了一个经验神经网络 (ENN),使用增强的Sooty Tern优化算法进行分类.
主要成果:
- 在心脏病预测方面实现了99.65%的分类准确度.
- 证明了增强的计算效率和改进的分类准确性.
- 在实时监控场景中验证了框架的有效性.
结论:
- 提出的基于心电图的框架对心脏病预测非常有效.
- 多阶段方法提高了基于物联网的医疗保健系统的性能.
- 该解决方案提供了一个可扩展和准确的实时心脏健康监测方法.
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