计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 588-590.

• 综合、交叉与应用 • 上一篇    下一篇

一种改进差分算法及其在QRS波检测中的应用研究

彭龑,吴兆强,张景扩,陈闰雪   

  1. 四川理工学院计算机学院 四川 自贡643000
  • 出版日期:2018-06-20 发布日期:2018-08-03
  • 作者简介:彭 龑 教授,主要研究方向为物联网技术及应用;吴兆强 硕士生,主要研究方向为物联网技术,E-mail:1468007872@qq.com;张景扩硕士生,主要研究方向为物联网技术;陈闰雪 硕士生,主要研究方向为物联网技术。
  • 基金资助:
    企业信息化与物联网测控技术实验室四川省高校重点实验室(2014WZY021)资助

Improved Difference Algorithm and It’s Application in QRS Detection

PENG Yan,WU Zhao-qiang, ZHANG Jing-kuo, CHEN Run-xue   

  1. School of Computer Science,Sichuan University of Science &Engineering,Zigong,Sichuan 643000,China
  • Online:2018-06-20 Published:2018-08-03

摘要: 文章使用一种改进的差分阈值算法来实现心电图QRS波的检测。实验证明,该算法的检测误差率在1%以下,同时还具有计算量小、实时性强等特点。区别于传统自适应算法,该算法能够在干扰较强的情况下实现QRS波的精确定位。算法实现如下:首先,通过一阶差分与二阶差分相结合的方法确定QRS波群;其次,通过自适应阈值确定Q,R,S峰的位置;最后,基于以上参数,采用窗体法确定出P波和T波的位置。

关键词: QRS波检测, 改进差分算法, 阈值自适应算法

Abstract: An improved difference threshold algorithm was used to realize the electrocardiogram QRS wave detection.Distinguishing from traditional adaptive algorithms,the algorithm can realize precise localization of QRS wave in the case of strong interference,the detection error rate is under 1%,and it has feature of a small amount of calculation and strong real-time.Through a lot of practice,the implementation of algorithm can be divided into three steps.Firstly,through the combination of first derivative and second derivative,the QRS complex is determined.Secondly,Q,R,S peak position are confirmed through the adaptive threshold.Thirdly,the position of P wave and T wave are determined by using the form method through above parameters.

Key words: Adaptive threshold algorithm, Improved difference algorithm, QRS detection

中图分类号: 

  • TP301.6
[1]YANG P,TIAN A Y,GUO X.Detection of QRS wave based on difference-slope method[J].Journal of Nanjing University of Science and Technology,2009,33(Suppl):128-132.
[2]ZENG Z Q,GE X H,WU Q.Simplifed support vectormachine method for QRS wave detection[C]∥IEEE 10th International Conference on Computer-Aided Industrial Design & Conceptual Design.2009:1427-1429.
[3]ABIBULLAEV B,SEO H D.A New QRS Detection Method Using Wavelets and Artificial Neural Networks[J].Journal of Medical Systems,2011,35(4):683-691.
[4]WEI W,LIU X W.Power interference removal method based on independent component analysis[J].Computer Application and Research,2009,26(1):227-229.
[5]CHOUAKRI S A,BEREKSI-REGUIG F,TALEB-AHMED A.QRS complex detection based on multi wavelet packet decomposition[J].Applied Mathematics & Computation,2011,217(23):9508-9525.
[6]ZHU L Y,LU X.A Novel Ambulatory ECG Compression Method Based on Feature Wave Detection and Down Sampling[C]∥IEEE Transactions on Biomedical Engineering.2011.
[7]HACKE M,VERMEIRE P.A low-cost general-purpose ana- logue QRS wave detector[J].Medical & Biological Engineering,1974,12(6),823-826.
[8]张永海.心电信号QRS波检测算法的研究[D].哈尔滨:哈尔滨工业大学,2008.
[9]YEH Y C,WANG W J.QRS complexes detection for ECG signal:The Difference Operation Method[J].Computer Methods and Programs in Biomedicine,2008,91(3):245-254.
[10]KHEMIRI S,ALOUI K,NACEUR M.Detection of Sleep Apnea in SWS Stages Using ECG Signal[C]∥Planetary Scientific Research Center Conference.2013:25-26.
[1] 邵子灏, 杨世宇, 马国杰.
室内信息服务的基础——低成本定位技术研究综述
Foundation of Indoor Information Services:A Survey of Low-cost Localization Techniques
计算机科学, 2022, 49(9): 228-235. https://doi.org/10.11896/jsjkx.210900260
[2] 孙刚, 伍江江, 陈浩, 李军, 徐仕远.
一种基于切比雪夫距离的隐式偏好多目标进化算法
Hidden Preference-based Multi-objective Evolutionary Algorithm Based on Chebyshev Distance
计算机科学, 2022, 49(6): 297-304. https://doi.org/10.11896/jsjkx.210500095
[3] 李丹丹, 吴宇翔, 朱聪聪, 李仲康.
基于多种改进策略的改进麻雀搜索算法
Improved Sparrow Search Algorithm Based on A Variety of Improved Strategies
计算机科学, 2022, 49(6A): 217-222. https://doi.org/10.11896/jsjkx.210700032
[4] 胡聪, 何晓晖, 邵发明, 张艳武, 卢冠林, 王金康.
基于极大极稳定区域及SVM的交通标志检测
Traffic Sign Detection Based on MSERs and SVM
计算机科学, 2022, 49(6A): 325-330. https://doi.org/10.11896/jsjkx.210300117
[5] 杨健楠, 张帆.
一种结合双注意力机制和层次网络结构的细碎农作物分类方法
Classification Method for Small Crops Combining Dual Attention Mechanisms and Hierarchical Network Structure
计算机科学, 2022, 49(6A): 353-357. https://doi.org/10.11896/jsjkx.210200169
[6] 张嘉淏, 刘峰, 齐佳音.
一种基于Bottleneck Transformer的轻量级微表情识别架构
Lightweight Micro-expression Recognition Architecture Based on Bottleneck Transformer
计算机科学, 2022, 49(6A): 370-377. https://doi.org/10.11896/jsjkx.210500023
[7] 王方红, 范兴刚, 杨静静, 周杰, 王德恩.
一种基于有向感知区域调整的强栅栏构建算法
Strong Barrier Construction Algorithm Based on Adjustment of Directional Sensing Area
计算机科学, 2022, 49(6A): 612-618. https://doi.org/10.11896/jsjkx.210300291
[8] 张志龙, 史贤俊, 秦玉峰.
基于改进准深度算法的诊断策略优化方法
Diagnosis Strategy Optimization Method Based on Improved Quasi Depth Algorithm
计算机科学, 2022, 49(6A): 729-732. https://doi.org/10.11896/jsjkx.210700076
[9] 高元浩, 罗晓清, 张战成.
基于特征分离的红外与可见光图像融合算法
Infrared and Visible Image Fusion Based on Feature Separation
计算机科学, 2022, 49(5): 58-63. https://doi.org/10.11896/jsjkx.210200148
[10] 刘洋, 李凡长.
基于变分贝叶斯的纤维丛元学习算法
Fiber Bundle Meta-learning Algorithm Based on Variational Bayes
计算机科学, 2022, 49(3): 225-231. https://doi.org/10.11896/jsjkx.201100111
[11] 乔杰, 蔡瑞初, 郝志峰.
一种基于信息瓶颈的因果关系挖掘方法
Mining Causality via Information Bottleneck
计算机科学, 2022, 49(2): 198-203. https://doi.org/10.11896/jsjkx.210100053
[12] 赵学磊, 季新生, 刘树新, 李英乐, 李海涛.
基于路径连接强度的有向网络链路预测方法
Link Prediction Method for Directed Networks Based on Path Connection Strength
计算机科学, 2022, 49(2): 216-222. https://doi.org/10.11896/jsjkx.210100107
[13] 魏昕, 冯锋.
基于高斯-柯西变异的帝国竞争算法优化
Optimization of Empire Competition Algorithm Based on Gauss-Cauchy Mutation
计算机科学, 2021, 48(11A): 142-146. https://doi.org/10.11896/jsjkx.201200071
[14] 王友卫, 朱晨, 朱建明, 李洋, 凤丽洲, 刘江淳.
基于用户兴趣词典和LSTM的个性化情感分类方法
User Interest Dictionary and LSTM Based Method for Personalized Emotion Classification
计算机科学, 2021, 48(11A): 251-257. https://doi.org/10.11896/jsjkx.201200202
[15] 肖满, 李伟东.
两点混合环上的半在线算法
Semi-online Algorithms for Mixed Ring with Two Nodes
计算机科学, 2021, 48(11A): 441-445. https://doi.org/10.11896/jsjkx.201100153
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!