文章基本信息

标题：Classification Of Ecg Arrhythmias Using Discrete Wavelet Transform and Neural Networks
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作者：Maedeh Kiani Sarkaleh ; Asadollah Shahbahrami
期刊名称：International Journal of Computer Science, Engineering and Applications (IJCSEA)
印刷版ISSN：2231-0088
电子版ISSN：2230-9616
出版年度：2012
卷号：2
期号：1
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Automatic recognition of cardiac arrhythmias is important for diagnosis of cardiac abnormalies. Several algorithms have been proposed to classify ECG arrhythmias; however, they cannot perform very well. Therefore, in this paper, an expert system for ElectroCardioGram (ECG) arrhythmia classification is proposed. Discrete wavelet transform is used for processing ECG recordings, and extracting some features, and the Multi-Layer Perceptron (MLP) neural network performs the classification task. Two types of arrhythmias can be detected by the proposed system. Some recordings of the MIT-BIH arrhythmias database have been used for training and testing our neural network based classifier. The simulation results show that the classification accuracy of our algorithm is 96.5% using 10 files including normal and two arrhythmias.
关键词：ECG; Arrhythmia; Daubechies Wavelets; Discrete Wavelet Transform (DWT); Neural Network (NN)