Majlesi Journal of Electrical Engineering Neural Network Based Method for Automatic ECG Arrhythmias Classification Volume 8 (2014) Issue 3, September 2014 2024 02 21 Neural Network Based Method for Automatic ECG Arrhythmias Classification EN Fereshteh Poorahangaryan Department of electrical engineering, Ayandegan Institute of Higher Education, Tonekabon, Iran Sona Morajab Department of electrical engineering, Ayandegan Institute of Higher Education, Tonekabon, Iran Azadeh Kiani Sarkaleh Department of Electrical Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran. Journal Article 2024 02 21 Automatic classification of electrocardiogram (ECG) arrhythmias is essential to timely and early diagnosis of conditions of the heart. In this paper, a new method for ECG arrhythmias classification using wavelet transform (WT) and neural networks (NN) is proposed. Here, we have used a discrete wavelet transform (DWT) for processing ECG recordings, and extracting some time-frequency features. In addition, we have combined the features extracted by DWT with ECG morphology and heartbeat interval features, to obtain our final set of features to be used for training a Multi-Layer Perceptron (MLP) neural network. The MLP Neural Network performs the classification task. In recent years, many algorithms have been proposed and discussed for arrhythmias detection. the results reported in them, have generally been limited to relatively small set of data patterns. In this paper 26 recordings of the MIT-BIH arrhythmias data base have been used for training and testing our neural network based classifier. The simulation results of best structure show that the classification accuracy of the proposed method is 94.72% over 360 patterns using 26 files including normal and five arrhythmias.