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基于智能床产生的加速计信号,用于检测心跳的机器学习算法的比较
Minh Long Hoang1, Guido Matrella1, Paolo Ciampolini1
1Department of Engineering and Architecture, University of Parma, 43124 Parma, Italy.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
随机森林机器学习卓越于使用智能床加速度计进行非侵入性心跳检测. 这种AI方法为持续的睡眠监测提供了高精度,优于深度学习模型.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 可穿戴技术可穿戴技术
背景情况:
- 在睡眠期间进行非侵入性,持续的心脏监测对于医疗保健和健康至关重要.
- 配备加速度计的智能床为不引人注目的生理信号采集提供了一个有前途的平台.
- 现有的方法往往需要直接接触,或者在连续监控能力方面受到限制.
研究的目的:
- 用智能床加速度计数据比较机器学习 (ML) 和深度学习 (DL) 算法的心跳检测性能.
- 在现实世界的睡眠监测设置中评估各种人工智能 (AI) 算法的有效性.
- 为了确定最准确和最有效的算法实时弹性心脏图谱心跳检测.
主要方法:
- 使用智能床上的3D固态加速度计进行数据采集,并使用光电显微镜进行地面真实检查.
- 通过STM 32位微控制器处理加速信号并传输到PC进行记录.
- 训练和评估多个ML和DL算法,使用来自10名参与者的数据集 (120分钟) 进行K折交叉验证.
主要成果:
- 随机森林算法在所有测试的ML和DL模型中实现了最高准确率 (>90%).
- 随机森林展示了卓越的性能指标,包括回忆,精度和F1分数.
- 虽然训练时间比一些简单的ML模型更长,但随机森林比支持矢量机和DL模型快得多.
结论:
- 随机森林算法是一种高效的解决方案,用于使用智能床数据实时弹性心跳图心跳检测.
- 它的高精度和强大的性能指标使其适合长期,非侵入性的睡眠监测.
- 这种方法显示了未来医疗保健和健康监测应用的巨大潜力.
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Direct Method
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