Integration of Hrv, Wt and Neural Networks for Ecg Arrhythmias Classification

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.