Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal
Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal
复制标题
DOI：
10.1016/j.artmed.2008.04.007
复制
发表时间：
2008-09-01
影响因子：
7.5
通讯作者：
Mohebbi, Maryam
中科院分区：
文献类型：
--
作者：
Asl, Babak Mohammadzadeh;Setarehdan, Seyed Kamaledin;Mohebbi, Maryam
目的：提出一种基于心率变异性（HRV）信号的心律失常分类算法。该算法基于广义判别分析（GDA）特征约简方案和支持向量机（SVM）分类器。方法：首先通过线性和非线性方法从输入的HRV信号中提取15个不同的特征。这些功能，然后减少到只有五个功能的GDA技术。这不仅减少了输入特征的数量，而且通过选择最有区别的特征来提高分类精度。最后，SVM结合一个对所有的策略被用来分类的HRV信号。结果：建议GDA和SVM的心律失常分类算法应用于输入的HRV信号，从MIT-BIH心律失常数据库中获得，区分六种不同类型的心律失常。特别地，代表包括正常窦性心律、室性早搏、心房纤颤、病窦综合征、心室纤颤和2度心脏传导阻滞的六种不同类型的心律失常类别的HRV信号分别以98.94%、98.96%、98.53%、98.51%、100%和100%的准确度被分类，结论：提出了一种有效的心律失常分类算法。与使用ECG信号本身的方法相比，所提出的算法的主要优点在于，它完全基于HRV（R-R间隔）信号，该HRV信号能够以相对高的准确度从甚至非常嘈杂的ECG信号中提取。此外，HRV信号的使用导致处理时间的有效减少，这提供了在线心律失常分类系统。然而，所提出的算法的主要缺点是，仅使用从HRV信号提取的特征不能检测诸如左束分支阻滞和右束分支阻滞搏动的一些心律失常类型。(C)2008 Elsevier B. V.保留所有权利。
Objective: This paper presents an effective cardiac arrhythmia classification algorithm using the heart rate variability (HRV) signal. The proposed algorithm is based on the generalized discriminant analysis (GDA) feature reduction scheme and the support vector machine (SVM) classifier.Methodology: Initially 15 different features are extracted from the input HRV signal by means of linear and nonlinear methods. These features are then reduced to only five features by the GDA technique. This not only reduces the number of the input features but also increases the classification accuracy by selecting most discriminating features. Finally, the SVM combined with the one-against-all strategy is used to classify the HRV signals.Results: The proposed GDA- and SVM-based cardiac arrhythmia classification algorithm is applied to input HRV signals, obtained from the MIT-BIH arrhythmia database, to discriminate six different types of cardiac arrhythmia. In particular, the HRV signals representing the six different types of arrhythmia classes including normal sinus rhythm, premature ventricular contraction, atrial fibrillation, sick sinus syndrome, ventricular fibrillation and 2 degrees heart block are classified with an accuracy of 98.94%, 98.96%, 98.53%, 98.51%, 100% and 100%, respectively, which are better than any other previously reported results.Conclusion: An effective cardiac arrhythmia classification algorithm is presented. A main advantage of the proposed algorithm, compared to the approaches which use the ECG signal itself is the fact that it is completely based on the HRV (R-R interval) signal which can be extracted from even a very noisy ECG signal with a relatively high accuracy. Moreover, the usage of the HRV signal leads to an effective reduction of the processing time, which provides an online arrhythmia classification system. A main drawback of the proposed algorithm is however that some arrhythmia types such as left bundle branch block and right bundle branch block beats cannot be detected using only the features extracted from the HRV signal. (C) 2008 Elsevier B.V. All rights reserved.