基于光纤光栅的呼吸测量及分类研究

Research on Respiratory Measurement and Classification Based on Fiber Bragg Grating

本文对基于光纤布拉格光栅(FBG)的呼吸测量及分类系统进行了研究。为了方便智能穿戴的需要,用聚二甲基硅氧烷对裸光纤光栅进行了封装,搭建呼吸监测系统,实现了呼吸信号的测量。采集了屏息、咳嗽、正常呼吸和运动后呼吸四种呼吸信号,基于小波分解与重构,对采集的呼吸信号进行预处理并提取呼吸信号的频率、振幅因数、波形因数和能量作为区分呼吸类型的特征。构建基于支持向量机(SVM)的呼吸分类模型,采用粒子群(PSO)优化SVM的模型参数,最后实现了97.1875%的分类准确率。该系统具有成本低、结构紧凑、设计简单等特点,可以丰富数字诊疗技术。

The respiratory measurement and classification system based on fiber Bragg grating is studied in this paper.In order to facilitate the needs of intelligent wear,the bare fiber grating was encapsulated with polydimethylsiloxane(PDMS),and the respiratory monitoring system was built to measure the respiratory signal.Four kinds of respiratory signals including breath-holding,cough,normal breathing and post-exercise breathing were collected.Based on wavelet decomposition and reconstruction,the collected respiratory signals were preprocessed and the frequency,amplitude factor,waveform factor and energy of the respiratory signals were extracted as characteristics to distinguish respiratory types.A respiration classification model based on support vector machine(SVM)was constructed,and the model parameters of SVM were optimized by particle swarm optimization.Finally,the classification accuracy was achieved at 97.1875%.The system is characterized by low cost,compact structure and simple design,which can enrich the digital diagnosis and treatment technology.