机器学习用于从可穿戴设备中分析生物信号
Inhea Jeong1,2, Won Gi Chung1,2, Enji Kim1,2
1Department of Materials Science and Engineering, Yonsei University, Seoul 03722, Republic of Korea. jang-ung@yonsei.ac.kr.
Materials horizons
|May 29, 2025
概括
机器学习 (ML) 通过改进生物信号分析来增强可穿戴生物电子设备的实时健康监测. 本综述指导选择ML模型以从复杂数据中获得准确的健康见解.
科学领域:
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
- 医疗信息学 医疗信息学
背景情况:
- 可穿戴生物电子设备可实现持续的健康监测和个性化洞察力.
- 生物信号数据由于体积,复杂性,噪声和工件而带来了挑战.
- 机器学习 (ML) 对于处理复杂的生物信号数据和发现模式至关重要.
研究的目的:
- 审查生物信号处理的关键ML算法.
- 为选择合适的ML模型提供准则.
- 讨论ML在健康监测和疾病预测中的应用.
主要方法:
- 探索用于生物信号处理的ML算法.
- 讨论数据预处理技术.
- 审查ML模型,包括集群,回归和分类.
- 对基于机器学习的分析的评估方法的检查.
主要成果:
- 确定ML模型选择的关键因素:数据特征,处理目标,计算效率和准确性.
- 跨神经,心血管和生化生物信号的ML应用概述.
- 突出 ML 与可穿戴生物电子设备的整合,以实现先进的健康监测.
结论:
- 机器学习对于克服分析可穿戴设备复杂生物信号数据的挑战至关重要.
- 仔细的模型选择和预处理是准确的ML驱动生物信号分析的关键.
- 机器学习与可穿戴生物电子技术的整合有望彻底改变医疗保健系统.
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