光纤光栅脉搏波分析处理及特征提取

本文提出了一种利用光纤光栅（Fiber Bragg Grating, FBG）检测脉搏波的信号处理及特征提取算法。将光纤光栅采集脉搏波与光电容积（Photoplethysmography, PPG）脉搏波进行对比，分析出光纤光栅脉搏波的特点。提出了小波阈值消噪与改进的数学形态学滤波相结合的光纤光栅脉搏波消噪算法，并根据脉搏周期对形态学结构元素长度进行自适应选择，从而改善了去除基线漂移的效果。研究了脉搏特征提取方法，提高了脉搏波峰值点和起点检测的准确性。实验结果表明，经消噪处理后，输出脉搏波的信噪比是输入脉搏波信噪比的2倍，脉搏波峰值点和起点提取准确率分别达到了97.2%和97.6%。该算法结构简单，易于实现，对光纤光栅脉搏波检测智能服装的研发和脉搏特征的有效提取具有重要的意义。 

Analysis and Feature Extraction of Pulse Wave Signal measured by FBG Sensor

This paper proposed a signal process and feature extract algorithm of human’s pulse wave signal collected by fiber Bragg grating (FBG) sensor. This paper compares the pulse wave signal collected by FBG sensor with the photoplethysmography (PPG) pulse wave and then figures out the characteristics of FBG pulse wave. A wavelet threshold de-noising combined with modified mathematical morphology is proposed, which is used in the signal de-noising works, and the length of mathematical morphology structural element is automatically selected based on the pulse cycle that can improve the effect of baseline de-noise. The feature extraction method proposed in this paper can improve the measuring accuracy of peak and start points of the pulse wave. According to the result of experiment, the Signal-to-noise Ratio(SNR) of the output pulse wave was twice as much as the SNR of the input pulse wave, and the accuracy of extracted peak and start points were over 97.2% and 97.6%. With simple structure and ease of implementation, this method is significance for the research of FBG smart clothing and the detection of pulse characteristics.