在物联网中使用基于心电图的轻量级系统进行心脏病预测,该系统基于元启发式方法
Amin Abbaszadeh1, Mahdi Bazargani1
1Department of Computer Engineering, Zanjan Branch, Islamic Azad University, Zanjan, Iran.
Heliyon
|December 13, 2024
概括
这项研究引入了使用优化机器学习模型准确分类心脏病的两步方法. 该方法在通过心电图信号检测心律失常时达到很高的准确性,有助于早期诊断.
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:
- 心血管疾病 (CVD) 是一个日益严重的全球健康问题,心脏病发作是导致死亡的主要原因.
- 准确和早期诊断心脏病 (HD) 是至关重要的,但由于复杂的症状,具有挑战性.
- 物联网 (IoT) 与可穿戴设备的整合为远程心脏监测提供了潜力,需要轻量级的诊断模型.
研究的目的:
- 为支持物联网的系统开发和评估一种新的,轻量级的两步心脏病分类方法.
- 用先进的机器学习技术提高心律失常诊断的准确性和效率.
- 通过强有力的两阶段方法来解决分类多种症状的心脏病的挑战.
主要方法:
- 预处理ECG信号以消除噪音并增强流性.
- 特性提取包括PQRS波浪,线性和相互特性.
- 一个两阶段的分类系统:使用优化的非线性支向量机器 (WHO-sigmoid-TH-NL-demarcation) 进行二进制分类,然后进行模糊逻辑分类 (GPC-TS-Fuzzy).
主要成果:
- 拟议的方法在MIT-BIH心律失常数据集上实现了高性能指标.
- 平均准确率为98.58%,敏感度为98.13%,特异性为96.47%.
- 两步分类方法在分类心律失常方面表现出有效性.
结论:
- 本文所介绍的两步心脏病分类方法对于检测心律失常是有效且准确的.
- 优化的机器学习模型 (WHO-sigmoid-TH-NL-demarcation和GPC-TS-Fuzzy) 提供了一个适合物联网应用的轻量级解决方案.
- 这种方法显示出改善心血管疾病的早期诊断和管理的巨大潜力.
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