Das Manab Kumar, Ari Samit
Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela, Orissa 769008, India.
Int Sch Res Notices. 2014 Sep 17;2014:178436. doi: 10.1155/2014/178436. eCollection 2014.
Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.
心电图（ECG）信号分类在心脏病临床诊断中起着重要作用。本文提出了一种高效的系统设计，用于使用混合特征对正常心搏（N）、室性异位心搏（V）、室上性异位心搏（S）、融合心搏（F）和未知心搏（Q）进行分类。本文针对心电图心搏分类提出了两种不同的特征提取方法：（i）基于S变换的特征以及时间特征；（ii）基于ST和WT的混合特征以及时间特征。提取的特征集使用多层感知器神经网络（MLPNN）进行独立分类。在来自麻省理工学院-贝斯以色列女执事医疗中心心律失常数据库44次记录的多个正常和异常心电图信号上评估性能。在这项工作中，使用美国医学仪器促进协会（AAMI）标准推荐的五类心电图心搏，比较了三种特征提取技术与MLP-NN分类器的性能。所提出的特征提取技术对N、S、F、V和Q的平均灵敏度性能分别为95.70%、78.05%、49.60%、89.68%和33.89%。实验结果表明，与其他现有特征提取技术相比，所提出的特征提取技术表现出更好的性能。
