脉搏波信号的特征提取方法研究
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摘要
脉搏波信号是重要的人体生理信号,但在采集过程中会存在一定的干扰,在分析软、硬阈值去噪方法的基础上,提出一种改进的阈值量化方法,能够克服软、硬阈值存在的缺陷,实验表明,改进的阈值方法可以有效地去除不同噪声干扰,明显优于软、硬阈值去噪方法。针对传统特征提取方法的缺陷,提出一种幅度阈值与滑动窗口相结合的主波波峰定位方法,经实验验证,所提出的方法准确率高、抗干扰强。
The pulse wave signal is an important human physiological signal, but there are some interference signals in the collection process. Based on soft and hard threshold de-noising method, an improved threshold function method is proposed. Improved threshold function can overcome the defects of soft and hard threshold method. The experimental results show that the improved threshold method can effectively remove noise, and gives better performance than the soft and hard threshold method.For the defects of the general feature extraction method, another method based on amplitude threshold and sliding window is proposed to detect the position of pulse wave crest. The experimental results show that the method is effective and accurate.
The pulse wave signal is an important human physiological signal, but there are some interference signals in the collection process. Based on soft and hard threshold de-noising method, an improved threshold function method is proposed. Improved threshold function can overcome the defects of soft and hard threshold method. The experimental results show that the improved threshold method can effectively remove noise, and gives better performance than the soft and hard threshold method.For the defects of the general feature extraction method, another method based on amplitude threshold and sliding window is proposed to detect the position of pulse wave crest. The experimental results show that the method is effective and accurate.
引文
[1]洋洋,陈小惠,王保强,姜吉荣.脉搏信号中有效信号识别与特征提取方法研究[J].电子测量与仪器学报,2016,30(01)126-132.
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[4]周聪聪.穿戴式生理参数监测关键技术研究及系统设计[D].浙江大学,2016.
[5]邓为贤.可穿戴多生理参数监测的数据处理平台设计与算法研究[D].天津大学,2016.
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[8]郭蔚,陈雅颂.自适应联合小波去噪算法[J].吉林大学学报(信息科学版),2007,25(2):145-150.
[9]孙薇,唐宁,江贵平.脉搏波信号特征点识别与预处理方法研究[J].生物医学工程学杂志,2015,32(01):197-201.
[10]李珊珊.基于脉搏波的穿戴式连续血压监测方法的研究[D].东南大学,2017.
[2]季忠,刘旭.基于波形特征和小波的脉搏波特征点识别研究[J].仪器仪表学报,2016,37(02):379-386.
[3]赵海,江理成,陈星池,王世艺.自适应脉搏波信号主峰定位方法[J].控制与决策,2018,33(03):509-513.
[4]周聪聪.穿戴式生理参数监测关键技术研究及系统设计[D].浙江大学,2016.
[5]邓为贤.可穿戴多生理参数监测的数据处理平台设计与算法研究[D].天津大学,2016.
[6]欧阳波,程栋,王玲.改进小波阈值算法在心电信号去噪中的应用[J].计算机工程与应用,2015,51(04):213-217.
[7]张诚,王一鹤,苗长云.光纤光栅脉搏波分析处理及特征提取[J].信号处理,2015,31(09):1145-1151.
[8]郭蔚,陈雅颂.自适应联合小波去噪算法[J].吉林大学学报(信息科学版),2007,25(2):145-150.
[9]孙薇,唐宁,江贵平.脉搏波信号特征点识别与预处理方法研究[J].生物医学工程学杂志,2015,32(01):197-201.
[10]李珊珊.基于脉搏波的穿戴式连续血压监测方法的研究[D].东南大学,2017.