基于决策树的光电容积脉搏波干扰段实时检测方法  

Real-time Detection Method for Motion Artifact of Photoplethysmography Signals Based on Decision Trees

作　　者：胡林奇 张宇林[1] 丑永新[2] 杨海萍 贺笑 HU Linqi;ZHANG Yulin;CHOU Yongxin;YANG Haiping;HE Xiao(Huaiyin Institute of Technology,Huaian,223003;Changshu Institute of Technology,Changshu,215500)

机构地区：[1]淮阴工学院,淮安市223003 [2]常熟理工学院,常熟市215500

出　　处：《中国医疗器械杂志》2024年第3期285-292,共8页Chinese Journal of Medical Instrumentation

基　　金：国家自然科学基金(61901062)。

摘　　要：光电容积脉搏波(photoplethysmography,PPG)在可穿戴和智能健康设备中具有重要的应用价值。然而,PPG信号在采集过程中由于不可避免的耦合运动产生干扰段,导致信号质量降低。面向PPG信号干扰段实时检测问题,该研究分析PPG信号干扰段产生原因及波形特征,对相邻波峰间的脉搏波数据提取7个特征,并利用双样本Kolmogorov-Smirnov检验来选择显著变化的特征,最后训练决策树模型对干扰段信号进行实时检测。设计实验,采集20名在校大学生的PPG信号构成实验数据集。实验结果表明,所提出方法的平均准确率为(94.07±1.14)%,比常用的干扰段检测算法的准确率和实时性更高。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.

关 键 词：光电容积脉搏波 干扰段 决策树 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]