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%。
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%.
出处
《计算机工程与应用》
CSCD
北大核心
2017年第23期203-207,共5页
Computer Engineering and Applications
基金
国家自然科学基金面上项目(No.61371062)
山西省回国留学人员科研资助项目(No.2013-032)
山西省国际合作项目(No.2014081029-01)
关键词
J波
支持向量机(SVM)
核函数
特征向量
变步长
J wave
Support Vector Machine(SVM)
kernel function
feature vectors
variable step size
