LI Sinan,WANG Suxin.An Automatic Recognition Algorithm for Feature Points of Photoplethysmography[J].Journal of medical biomechanics,2019,34(4):358-364
一种光电容积脉搏波的特征点自动识别算法
An Automatic Recognition Algorithm for Feature Points of Photoplethysmography
Received:September 09, 2018  Revised:September 25, 2018
DOI:
Chinese key words:  特征点识别  光电容积脉搏波  上升支单调特性  希尔伯特变换
English Key words:feature point recognition  photoplethysmography  monotonic characteristics of ascending branch  Hilbert transformation
Fund project:河北民族师范学院博士科研启动基金项目(DR201601),河北民族师范学院普通基金项目(PT2017008)
Author NameAffiliation
LI Sinan Department of Physics and Electronic Engineering, Hebei Normal University for Nationalities 
WANG Suxin Department of Physics and Electronic Engineering, Hebei Normal University for Nationalities 
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Chinese abstract:
      目的 为弥补现有光电容积脉搏波特征点识别算法存在的需要人为设定阈值筛选门限和对复杂波形适应能力较差的缺陷,提出一种基于脉搏波上升支单调增加几何特性的特征点自动识别算法。方法 通过两次Hilbert变换后过零点检测在每个脉搏周期内确定一个“基准点”,在“基准点”前后搜索距离其最近的凹拐点即为波谷点、凸拐点即为主波峰点。结果 利用MIT-BIH标准数据库中18组数据进行检测验证,平均值达到99.94%灵敏度、99.72%查准率和99.68%检测准确率。对比已有的4种算法,在查准率上有明显的提升,应对复杂的波形依然能准确识别特征点。结论 提出的算法在搜索确定脉搏波波谷点和主波峰值点位置过程中取得较高的检测准确率,同时展现出对波形变化更强的适应力。研究结果为临床上通过脉搏波特征提取进行生理病理分析提供良好基础。
English abstract:
      Objective In order to make up for the deficiency in the existing photoplethysmography feature point recognition algorithms which need manually setting the selecting threshold and have poor adaptability to complex waveforms, an automatic reognition algorithm for feature points based on monotonic increase in geometrical characteristics of pulse wave ascending branch was proposed. Methods A ‘reference point’ was determined in each pulse period by zero crossing detection after two Hilbert transformation. The nearest concave and convex inflection points that searched around the ‘reference points’ were the notchs and systolic peaks. Results By using the 18 sets of data in the MIT-BIH standard database for verification, the average sensitivity, precision and detection accuracy reached 99.94%, 99.72% and 99.68%, respectively. Compared with the existing four algorithms, there was a significant improvement in the precision. Feature points could still be accurately identified for complex waveforms. Conclusions The proposed algorithm achieved a higher detection accuracy in the process of searching and determining the position of the pulse wave notchs and systolic peaks, and exhibited a stronger adaptability to the waveform change. The research results provide a good foundation for physiological and pathological analysis through pulse wave features extraction in clinic.
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