计算机工程与应用 ›› 2017, Vol. 53 ›› Issue (23): 203-207.DOI: 10.3778/j.issn.1002-8331.1604-0213

• 工程与应用 • 上一篇    下一篇

基于变步长支持向量机的J波自动检测方法

刘学博,李灯熬,赵菊敏,廖述剑   

  1. 太原理工大学 信息工程学院,太原 030024
  • 出版日期:2017-12-01 发布日期:2017-12-14

J wave auto-detection based on variable step size support vector machine

LIU Xuebo, LI Deng’ao, ZHAO Jumin, LIAO Shujian   

  1. College of Information Engineering, Taiyuan University of Technology, Taiyuan 030024, China
  • Online:2017-12-01 Published:2017-12-14

摘要: 近期研究表明J波综合征的某些类型成为恶性心律失常、心脏性猝死等疾病的高危预警新指标。但目前大多数的研究仅通过心电图纸和临床经验来判断是否存在J波,不仅耗时较长,而且难免会存在误判。从信号处理的角度出发,提出了一种基于变步长思想的支持向量机(Support Vector Machine,SVM)方法来实现J波的自动检测。利用变步长思想寻找最优的核函数参数[σ]并完支持向量机的建模。实验结果证明,所提的方法可以有效地检测出J波,准确率为96.1%。

关键词: J波, 支持向量机(SVM), 核函数, 特征向量, 变步长

Abstract: Recent researches have proved that some types of J-wave syndrome are high-risk indicators of malignant arrhythmia, sudden cardiac death, and other illnesses. But the judgment of whether J-wave exists in most of the present researches relies only on electrocardiograms and clinical experience, which not only are time-consuming but can hardly avoid misdiagnoses. This paper, from the perspective of signal processing, proposes an approach for J wave auto-detection based on the variable step size support vector machine. After the optimal value of parameter [σ]of kernel function is fixed, SVM modelling is completed. Experimental results show the effectiveness of J-wave detection by this method, with the accuracy rate reaching high at 96.1%.

Key words: J wave, Support Vector Machine(SVM), kernel function, feature vectors, variable step size