基于脉搏波特征的形成机理和分布特点，对采用PDMS封装的光纤光栅柔性传感器检测的人体手腕脉搏波信号，针对脉搏波最常出现的特征点明显、隐蔽和部分明显四种类型，提出基于时域微分周期比的脉搏波信号特征提取方法.采用各特征点在脉搏波时域微分信号中的相对位置及比例关系作为特征参数，实现了从脉搏波检测到特征点提取的综合算法.结果表明，对采集的4 050份实验数据，该算法能够全部准确识别起点与波峰的特征点，在静息状态下，潮波d、e点的识别准确率为98.28%和97.25%，重搏波f、g点的识别准确率为98.14%和99.19%；在运动状态下，潮波d、e的识别准确率分别为94.23%和90.77%，重搏波f、g识别准确率分别为91.93%和95.38%.

Based on the formation mechanism and distribution characteristics of the pulse wave characteristics， this paper proposes to use PDMS packaged fiber grating flexible sensor to detect the human wrist pulse wave signal， aiming at the four most common types of pulse waves： obvious， hidden and partly obvious. The pulse wave signal feature extraction method based on the time-domain differential period ratio uses the relative position and proportional relationship of each feature point in the pulse wave time-domain differential signal as feature parameters， and realizes a comprehensive algorithm from pulse wave detection to feature point extraction. The results show that for the 4 050 pieces of experimental data collected， the algorithm can accurately identify the characteristic points of the starting point and the crest. In the resting state， the identification accuracy of the tidal wave d and e points is 98.28% and 97.25%. The recognition accuracy rates of points f and g are 98.14% and 99.19%； in the state of exercise， the recognition accuracy rates of tidal waves d and e are 94.23% and 90.77%， respectively， and the recognition accuracy rates of dicrotic waves f and g are 91.93% and 95.38% respectively.