使用基于加速-减速曲线的神经网络的心脏病检测,使用消费级智能手表数据
Arman Naseri1,2, David M J Tax2, Marcel Reinders2
1Department of Cardiology, Haga Teaching Hospital, The Hague, Netherlands.
Heliyon
|November 18, 2024
概括
通过机器学习分析的智能手表数据可以帮助检测心血管疾病 (CVD). 加速-减速曲线显示出排除CVD的希望,但需要仔细处理数据和模型选择才能准确预测.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的机器学习
- 心血管医学 心血管医学
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡和残疾原因.
- 使用消费者智能手表的持续监控为心血管疾病检测提供了一种新的方法.
- 从可穿戴设备分析大量时间序列数据,由于信息细分稀少,因此存在挑战.
研究的目的:
- 评估基于加速减速曲线的机器学习 (ML) 模型,用于检测心血管疾病.
- 调查新型数据预处理和模型聚合技术对心血管疾病预测的有效性.
主要方法:
- 利用了ME-TIME研究的数据 (42名参与者:21名患有心血管疾病,21名健康对照).
- 通过峰值不活动曲线值应用每人对象的规范化.
- 采用神经网络模型,每周预测聚合和对比损失.
主要成果:
- 该模型在开发集上实现了99%的特异性和40%的灵敏性.
- 该模型在测试组上显示了100%的特异性和67%的灵敏性.
- 当数据被正确处理时,加速减速曲线有效排除了心血管疾病的存在.
结论:
- 加速-减速曲线对于排除心血管疾病有价值.
- 仔细预处理曲线和适当的模型选择对于减少变化和提高预测准确性至关重要.
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