在不受限制的日常活动期间,评估可穿戴设备的光电显微镜信号质量
Liang Wei1, Yushun Gong1, Yunchi Li1
1Department of Biomedical Engineering and Imaging Medicine, Army Medical University, Chongqing 400038, People's Republic of China.
Biomedical physics & engineering express
|November 27, 2025
概括
这项研究开发了一种强大的信号质量评估算法,用于可穿戴设备中的光电显微镜 (PPG) 信号. 该算法在各种日常活动中准确检测PPG信号质量,提高生理监测可靠性.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
背景情况:
- 摄影电磁镜 (PPG) 对于可穿戴健康监测至关重要,它可以跟踪心率和血液氧和等生命体征.
- 运动工件和噪音显著损害了PPG信号的准确性,导致在持续监测中潜在的误诊.
- 现有的信号质量评估 (SQA) 方法在现实世界的门诊环境中缺乏稳定性.
研究的目的:
- 为专门为可穿戴设备设计的PPG信号开发和验证一个强大的SQA算法.
- 确保在不受限制的日常活动中,包括不同强度水平,进行准确的生理测量.
- 提高基于PPG的健康监测系统的可靠性.
主要方法:
- 在各种日常活动中,从54名参与者获得了同步的PPG和加速信号.
- 标注信号质量 (好,中等,差) 和活动强度 (静止到剧烈) 的 35,712 个 5 秒 PPG 时代.
- 从形态,时间频率,一致性和统计领域提取了14个特征;训练了四个机器学习模型 (随机森林,SVM,天真贝叶斯,BP神经网络).
主要成果:
- 随机森林模型实现了最高的测试准确度 (95.6%),SVM (95.4%),BP NN (95.1%) 和Naive Bayes (94.1%) 的性能可比.
- 在静坐 (95.8%),轻度 (95.3%),中度 (96.0%) 和强度 (100%) 的活动水平上,分类准确度仍然很高,并且在统计学上一致.
- 所有选定的特征在不同的PPG信号质量水平之间显示出显著差异 (p < 0.05).
结论:
- 拟议的功能集和机器学习方法为可穿戴设备中的PPG信号提供了强大的SQA.
- 该算法在所有活动强度中保持高精度,确保可靠的生理数据收集.
- 这种稳定的性能促进了实时实施,提高了可穿戴健康监测的准确性.
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