可穿戴心电图设备和机器学习用于心脏监测
Zhadyra Alimbayeva1,2, Chingiz Alimbayev1,3, Kassymbek Ozhikenov1
1Department of Robotics and Technical Means of Automation, Satbayev University, Almaty 050013, Kazakhstan.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
这项研究引入了一种使用机器学习进行早期心脏病预测的新型可穿戴心电图监测系统. 卷积神经网络 (CNN) 在分析心血管疾病风险的ECG数据方面取得了高准确性.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 人工智能的人工智能
背景情况:
- 心血管疾病 (CVD) 是死亡的主要原因,增加了对有效监测解决方案的需求.
- 可穿戴设备为持续的心脏活动评估提供了一个有希望的途径.
- 现有的系统需要提升早期疾病预测能力.
研究的目的:
- 开发和评估一个创新的单线电图监测系统,与机器学习集成,用于早期心血管疾病预测.
- 评估各种机器学习算法的性能,分析ECG数据以检测异常和预测疾病.
- 从可穿戴设备到基于Web的接口建立实时数据处理管道.
主要方法:
- 使用ADS1298和STM32L151xD微控制器开发可穿戴式心电图系统.
- 实现信号处理算法:带通波,K-means聚类和PQRST分析.
- 机器学习模型的应用,包括隔离森林用于异常检测和对CNN,SVM,XGBoost和其他人的比较分析,用于CVD预测.
主要成果:
- 该系统成功地实时处理和分析了心电图数据.
- 隔离森林在ECG异常检测方面表现出有效性.
- 卷积神经网络 (CNN) 在预测心血管疾病方面实现了0.926的高准确性,超过了其他测试方法.
结论:
- 开发的可穿戴心电图系统,增强了机器学习,显示了早期心血管疾病检测的巨大潜力.
- 在处理心电图数据和预测心血管疾病发病率方面,CNN非常有效.
- 这种综合系统为远程心脏监测和主动医疗保健提供了一个有前途的工具.
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