从可穿戴式监控心率数据通过自行回归模型进行实时预测
Giulio De Sabbata1, Giovanni Simonini1
1University of Modena and Reggio Emilia, Modena, Italy.
Journal of healthcare informatics research
|May 1, 2025
概括
使用历史数据进行单变心率 (HR) 预测,为短期健康监测提供有限的预测能力. 即使是像ARIMA这样的优化模型也比简单的随机步行显示出极小的改进,质疑它们对报警系统的实用性.
科学领域:
- 心血管健康监测 监测心血管健康
- 生物医学信号处理
- 时间序列分析时间序列分析
背景情况:
- 通过可穿戴设备持续监测心率 (HR) 对心血管健康至关重要.
- 为早期检测系统探索HR的单变量预测.
- 自行回归集成移动平均 (ARIMA) 模型通常使用,但通常没有超参数优化.
研究的目的:
- 批判性地评估历史人力资源数据的预测能力,用于短期,单变量预测.
- 调查ARIMA超参数优化对HR预测准确性的影响.
- 确定过去的人力资源数据对准确的短期人力资源预测的贡献.
主要方法:
- 从日常工作中分析现实世界HR时间序列数据.
- 一分钟一分钟的无变预测方法.
- 超参数调整ARIMA模型以优化预测性能.
主要成果:
- 随机步行模型,简化的ARIMA案例,性能与复杂的ARIMA模型相比或更好.
- 优化的ARIMA模型没有显著提高短期HR预测准确度,而不是更简单的模型.
- 仅仅历史的人力资源数据就证明了短期预测的预测能力有限.
结论:
- 单变量HR预测对实时健康监测和报警系统具有固有的局限性.
- 增加单变量模型的复杂性可能不会提高预测能力.
- 未来的研究应该探索替代,潜在的多变量,改善早期预警系统的方法.
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