基于支持向量机的室颤信号检测算法  

Ventricular Fibrillation Detection Algorithm Based on Support Vector Machine

作　　者：张春云[1] 赵捷[1] 贾慧琳[1] 李斐[1] 

机构地区：[1]山东师范大学物理与电子科学学院,山东济南250014

出　　处：《现代生物医学进展》2012年第9期1751-1754,1768,共5页Progress in Modern Biomedicine

基　　金：山东省自然科学基金(ZR2010HM020);济南市科技发展计划项目(201102005)

摘　　要：目的:实现室颤信号与非室颤信号的分类,进而实现室颤信号的检测。方法:本文引入了一种基于支持向量机(Support Vec-tor Machine,SVM)和改进的越限区间算法(TCI)的新算法,其中支持向量机在处理分类和模式识别等问题中具有很大的优势。该算法采用4s的滑动窗技术,并利用改进后的越限区间算法(Threshold Crossing Interval,TCI)方法提取心电信号的特征。新算法的实现如下:在每一滑动窗内采用改进的后的绝对值阈值,计算中间2s内的平均越限间隔值。并以此TCI值作为特征参数,输入一个预先设计好的二分类支持向量机中,从而实现分类。结果:成功实现了室颤信号的检测,通过计算该方法的灵敏度、精确度、预测性和准确度且与其他方法相比较,可知此新算法总体可靠性优于其他方法。结论:该算法能够实现室颤信号的实时监测,且简单易行,易于实现,较适合实时的心电监测以及除颤仪器。Objective：To realize the discrimination of ventricular fibrillation（VF） and non-ventricular fibrillation（non-VF）,accordingly detection of VF.Methods： The new algorithm was based on support vector machine（SVM） and the improved（TCI） algorithm.The SWM has great advantages in processing classification and pattern recognition.The new algorithm utilized 4-s-sliding-window technology and the improved TCI to extract features of ECG.It was implemented as follows： by using absolute thresholds,calculated average threshold crossing intervals of the middle 2s segment in every sliding window,and then input the TCI values into a binary classification support vector machine,finally,the discrimination was realized.Results： VF and non-VF were classified successfully.It shows that the new algorithm was superior to other classical algorithms by calculating quality parameters.Conclusions： This new algorithm can be used for real time VF detection.It is easier to implement and has greater advantages in real-time execution.It is suitable for ECG monitoring and defibrillator.

关 键 词：室性纤颤(VF) TCI 支持向量机(SVM) 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]