Asl Babak Mohammadzadeh, Setarehdan Seyed Kamaledin, Mohebbi Maryam
Department of Biomedical Engineering, Tarbiat Modares University, Tehran, Iran.
Artif Intell Med. 2008 Sep;44(1):51-64. doi: 10.1016/j.artmed.2008.04.007. Epub 2008 Jun 27.
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.
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.
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.
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.
本文提出一种使用心率变异性（HRV）信号的有效心律失常分类算法。所提出的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。
首先通过线性和非线性方法从输入的HRV信号中提取15种不同特征。然后利用GDA技术将这些特征约简为仅5个特征。这不仅减少了输入特征的数量，还通过选择最具判别力的特征提高了分类准确率。最后，结合一对多策略的SVM用于对HRV信号进行分类。
所提出的基于GDA和SVM的心律失常分类算法应用于从麻省理工学院 - 贝斯以色列女执事医疗中心（MIT - BIH）心律失常数据库获得的输入HRV信号，以区分六种不同类型的心律失常。具体而言，代表六种不同心律失常类别的HRV信号，包括正常窦性心律、室性早搏、心房颤动、病态窦房结综合征、心室颤动和二度房室传导阻滞，分类准确率分别为98.94%、98.96%、98.53%、98.51%、100%和100%，优于之前报道的任何其他结果。
提出了一种有效的心律失常分类算法。与使用心电图（ECG）信号本身的方法相比，所提出算法的一个主要优点是它完全基于HRV（R - R间期）信号，即使从非常嘈杂的ECG信号中也能以相对较高的准确率提取该信号。此外，使用HRV信号可有效减少处理时间，从而提供一个在线心律失常分类系统。然而，所提出算法的一个主要缺点是，仅使用从HRV信号中提取的特征无法检测某些心律失常类型，如左束支传导阻滞和右束支传导阻滞搏动。
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