Hu Linqi, Zhang Yulin, Chou Yongxin, Yang Haiping, He Xiao
Huaiyin Institute of Technology, Huaian, 223003.
Changshu Institute of Technology, Changshu, 215500.
Zhongguo Yi Liao Qi Xie Za Zhi. 2024 May 30;48(3):285-292. doi: 10.12455/j.issn.1671-7104.230552.
PPG (photoplethysmography) holds significant application value in wearable and intelligent health devices. However, during the acquisition process, PPG signals can generate motion artifacts due to inevitable coupling motion, which diminishes signal quality. In response to the challenge of real-time detection of motion artifacts in PPG signals, this study analyzed the generation and significant features of PPG signal interference. Seven features were extracted from the pulse interval data, and those exhibiting notable changes were filtered using the dual-sample Kolmogorov-Smirnov test. The real-time detection of motion artifacts in PPG signals was ultimately based on decision trees. In the experimental phase, PPG signal data from 20 college students were collected to formulate the experimental dataset. The experimental results demonstrate that the proposed method achieves an average accuracy of (94.07±1.14)%, outperforming commonly used motion artifact detection algorithms in terms of accuracy and real-time performance.
光电容积脉搏波描记法（PPG）在可穿戴和智能健康设备中具有重要的应用价值。然而，在采集过程中，由于不可避免的耦合运动，PPG信号会产生运动伪影，从而降低信号质量。针对PPG信号中运动伪影的实时检测挑战，本研究分析了PPG信号干扰的产生及显著特征。从脉搏间期数据中提取了七个特征，并使用双样本柯尔莫哥洛夫-斯米尔诺夫检验对那些表现出显著变化的特征进行过滤。PPG信号中运动伪影的实时检测最终基于决策树。在实验阶段，收集了20名大学生的PPG信号数据以形成实验数据集。实验结果表明，所提出的方法实现了（94.07±1.14）%的平均准确率，在准确率和实时性能方面优于常用的运动伪影检测算法。
