脉冲2AI:一种适应性框架,用于标准化和处理用于临床应用的脉冲可穿戴传感器数据
Sicong Huang1, Roozbeh Jafari2,3,4,5, Bobak J Mortazavi1
1Department of Computer Science and EngineeringTexas A&M University College Station TX 77840 USA.
IEEE open journal of engineering in medicine and biology
|June 20, 2024
概括
Pulse2AI是一个新的数据预处理框架,将原始可穿戴传感器数据转换为机器学习的高质量数据集. 这一框架显著提高了远程健康监测任务的准确性,例如估计血压和呼吸速率.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 可穿戴设备产生大量的脉冲信号数据.
- 现有的预处理方法缺乏适应性和任务不可知能力.
- 高质量,机器学习准备的数据集对于推进远程健康监测至关重要.
研究的目的:
- 推出Pulse2AI,这是一个可重复的数据预处理框架,用于脉冲信号.
- 从原始可穿戴录音生成高质量,机器学习准备的数据集.
- 创建一个能够适应多种脉动信号模式的框架,并且独立于下游医疗任务.
主要方法:
- 开发了一个名为Pulse2AI的端到端数据预处理框架.
- 设计了框架,使其不依赖于特定的下游医疗应用.
- 验证了框架在各种脉动信号类型中的适应性.
主要成果:
- 脉冲2AI预处理提高了29.58%的缩血压估计精度 (RMSE从11.41到8.03mmHg).
- 透析血压估计精度提高了26.01% (RMSE从7.93到5.87mmHg).
- 呼吸速率估计性能提高了19.69% (MAE从1.47升至1.18 BrPM).
结论:
- Pulse2AI有效地将脉动信号转化为机器学习准备的数据集,用于各种远程健康监测.
- 该框架在医疗应用中表现出有效性,增强了血压和呼吸速率估计.
- Pulse2AI弥合了医疗建模的遥感/IoMT和ML准备数据集之间的差距.
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