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.
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). They are
varied and the choice of types and number of parameters to approach them are also varied, significant that characterize the arrhythmia in question. It is possible to define the premature ventricles) in cardiac arrhythmias by the irregularity of the beats   with cardiac arrhythmias or as a group of different parameters, approaches and results.
On the other hand, several classification methods have been proposed in the literature to classify the arrhythmic beats in normal or abnormal (arrhythmic) beats for some and others according to different rhythms (TV and BV)  ,  and  compares three different procedures to characterize the rhythms to be classified by an LVQ network. But in most cases the essential parameters are measured around the QRS complex (width, shape and amplitude) and more particularly the detection of the R wave which is a dominant parameter , , . Especially for atrial arrhythmias (atrial fibrillation) the P wave and the PR interval are privileged.
Figure 1: Characteristics of a heartbeat
This varied study allowed us to establish a number of significant parameters to classify the arrhythmias present in the QRS complex.
Inspired by several works cited, we propose an approach of automatic recognition to detect several arrhythmias. It involves methods of non-linear analysis by neural networks and tools of non-stationary analysis by the wavelet transform (WT) which have very interesting adaptation properties.
Currently the wavelet method has become a technique, par excellence, very effective to extract the parameters that characterize the arrhythmia in its various forms. Many works using wavelets have been successfully carried out, in particular the articles ,  and  have amply demonstrated the contribution of the ECG signal. It makes it possible to produce the start and end times of waves and intervals and to filter the signal.
Nonlinear array analysis of artificial neurons (ANN) seems promising to provide indicators relevant to the identification of pathologies Heart disease. The literature is rich in works that have exploited the advantages of ANN to automatically detect or classify cardiac arrhythmias such as ,  , , , ,  and  where different classification approaches were discussed. But a classifier can be effective for an ECG and fail with another ECG or other pathology. This justifies the large number of works in this field. There will always be investigations because the slightest hope of improving performance saves a life. Previously Nath et al.  shown that for a particular study it is often difficult to identify the best classifier for lack of proper guide. Many comparative studies of classifiers are conducted. The combination of different approaches can be a powerful tool to improve the quality of classification algorithms. In particular, methods that take into account the a priori knowledge of the data have been more successful. This is why we propose vector quantization (VQ) learning algorithms whose essential tool is the codebook. By constructing the codebook, the VQ takes into account the specificities of the given application and thus makes it possible to exploit the information a priori. This is the combination of the SOM-LVQ algorithms and that of the RBF network. Each network (ANN) having learned the key characteristics of each arrhythmia, receives as input, in the test phase, a vector of parameters (defining a heartbeat) coming from the WT, discriminates in two or more classes of arrhythmias.
Our results are evaluated on the ECG recordings of MIT-BIH Data Arrhythmias, well known by ECG users, for detecting the following pathologies: Extra ventricular (V), Extra supraventricular systolic (S), Fibrillation Ventricular flutter (V), ventricular tachycardia (VT) and ventricular bradycardia (Br), which we will classify according to the rhythm and the frequency, the symbols are those used in MIT-BIH annotation.
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.
The slightest improvement provided by the algorithms is a good result because it involves saving a life. We can say that our procedure also has important potentialities, but in order to be able to be the subject of a method of aid to the diagnosis (applicable clinically) one must first treat more ECGs, in particular for the rhythm, to do Vary the database and treat other cardiac pathologies. Theoretically, neural networks are optimum when the database is very large. It is necessary to find explanations for the contradictory results between the two networks.
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Christos Stergiou and Dimitrios Siganos, “Neural Networks”, Report available at: http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html
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