VOL. 6, NO. 5, MAY 2011 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences © 2006-2011 Asian Research Publishing Network (ARPN). All rights reserved. www.arpnjournals.com INTEGRATION OF HRV, WT AND NEURAL NETWORKS FOR ECG ARRHYTHMIAS CLASSIFICATION A. Dallali, A. Kachouri and M. Samet Laboratory of Electronic and Technology of Information (LETI) National School of Engineers of Sfax, BP W 3038, Sfax, Tunisia Email: dallali_a@voila.fr ABSTRACT The classification of the electrocardiogram registration (ECG) into different pathologies disease devises is a complex pattern recognition task. The registered signal can be decomposed into three components, QRS complex, P and T waves. The QRS complex represent the reference for the other ECG parameters; the width and amplitude QRS have more important to identify the ECG pathologies. The statistical analysis of the ECG indicate that they differ significantly between normal and abnormal heart rhythm, then, it can be useful in detection of ECG arrhythmia. The traditional methods of diagnosis and classification present some inconvenient; seen that the precision of credit note one diagnosis exact depends on the cardiologist experience and the rate of concentration. Due to the high mortality rate of heart diseases, early detection and precise discrimination of ECG arrhythmia is essential for the treatment of patients. During the recording of ECG signal, different form of noises can be superimposed in the useful signal. The pre-treatment of ECG imposes the suppression of these perturbation signals, three methods for the noisily of signals are used; temporal, frequency, and time frequency method filter. The features are extracted from wavelet decomposition of ECG signal intensity. The inclusion of Artificial Neural Network (ANN) based on feed forward back propagation with momentum, in the diagnostic and classification of ECG pathologies have very important yield [1, 2]. The four parameters considered for ECG arrhythmia classification are the interval RR, the QRS width, the QRS amplitude, and the frequency of appears QRS. Due to the large amount of input data, needed to the classifier, the parameters are grouped in batches introduced to artificial neural network. The classification accuracy of the ANNs introduced classifier up to 90.5% was achieved, and a 99.5% of sensitivity. Keywords: cardiac pathologies, ECG, heart rate variability, wavelet transform, ANNs, classification. INTRODUCTION In recent years, computer assisted ECG interpretation has played an important role in automatic diagnosis of heart anomalies [1, 3]. The wave forms of ECG; width reflects the physical condition of human heart, is the most biological signal to study and diagnosis cardiac dysfunctions. So, it is important to record the patient’s ECG for a long period of time for clinical diagnosis. The clinical significance diagnosis depends on different parameters of ECG; complex QRS, wave P, frequency, Heart Rate Variability R-R. In these applications, it is more important to develop signal processing methods that permit real time feature extraction and de - noising of the ECG characteristic. The extracted parameters are used for the classification of the cardiac pathologies and make an automatic tool of diagnosis in the services of doctors before the arrival of a quantified patient. Many techniques were used for the diagnosis of ECG signal; temporal methods [4, 5], frequency method [4] and time frequency methods [5, 6]. The real time records of ECGs are accompanied by a high frequency signals that superposed with the useful ECG. The suppression of these perturbation signals is necessary to a performance classifier system. The ECG data must be filtered in order to attenuate undesired electrical components of ECG. Over recent years, wavelets transforms play an increasing role in the pre-processing medical signal. The ECG signals are filtered by band pass filters based and discrete wavelet transform. In the recent years, various algorithms are developed for classification and identification of the ECG anomalies. These algorithms are most based in fuzzy logic and Neural Network techniques. The remaining of the paper is organized as follows: The first stage, point out to the materials and methods used. In this stage, we present the ECG signal and their significant parameters for diagnostic. In the second stage, time and frequency domain are applied to de-noising ECG signal and extract the corresponding features. The extracted features are used to train an ANNs for classification of different anomalies is will be treated in third stage. The simulation results of the neural network classifier will be discussed at the end of the paper. 74