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This paper proposes a Neural Network classifier model for the automatic identification of cardiac arrhythmias. Our main aim is to have a better classification rate in a minimum time with simple algorithms in their implementation. The wavelet transform is used to extract the parameters which characterize different arrhythmic pathologies and thus constitute the bases of learning and generalization of the networks of neurons. A pre-processing by filtering adapted by the Butterworth Filter is applied to the signals before the extraction. Classification is done using Neural Network classifier further dimension reduction to reduce the overhead of the training is performed by PCA. The ECG signals used in this study are taken from the internationally known MIT-BIH Arrhythmia Database. The results obtained are satisfactory with average recognition rates of 95.7% in NN and 97.1 % for PCA-NN.
Cardiac arrhythmias, DWT, MIT-BIH, Neural Network, QRS.
Irregular beats form two groups: the first comprises fibrillation and ventricular tachycardia which are dangerous and the ECG signal represents the electrical activity of the need for immediate therapy with a heart, is probably the diagnostic technique of the defibrillator; the second group concerns the most common cardiac pathologies. ECG is non-hazardous arrhythmias but which require a non-stationary randomized signal, structured preventive therapy of other problem. These disorders (Figure 1) by the succession of wave and cardiac forms are also classified according to the frequency and the interval of the (P, Q, R, S, and T). Any rhythm modification described by the number of cycles or morphological or temporal contractions of its events (or ventricular (depolarization of the ventricles) by chronic) constitutes a cardiac pathology. The minutes, marked on the ECG by the number of changes concerning the complex rhythm or frequency QRS are cardiac arrhythmias (fatal diseases). A number of works , , , , , , and  have been successfully performed for clinical and technological problem highlighting arrhythmias, but they defy by (means and approaches).
The purpose of our work is to automatically classify detected cardiac arrhythmias on real ECG signals from the MIT-BIH database. The choice of nonlinear classification by artificial neural networks gives good performances because of their separation power from complex situations such as supraventricular arrhythmias. The performance of ANN also strongly depends on the quality of QRS extraction. The work that improved the extraction of the characteristics has had high results. The detection of the wavelet start and end of wavelets has been very effective for the QRS complex. This has guided our choice of approach. It is found that the efficiency of detection is nearly around 95.7% as the data is compared with MIT-BIH database.
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