Abstract
Cardiovascular diseases are the major public health parameter; they are the leading causes of mortality in the world. In fact many studies have been implemented to reduce the risk, including promoting education, prevention, and monitoring of patients at risk. In this paper we propose to develop classification system heartbeats. This system is based mainly on Wavelet Transform to extract features and Kohonen self-organization map the arrhythmias are considered in this study: N,(Normal), V(PrematureVentricular), A(AtrialPremature), S(Extrasystolesupraventriculaire), F(FusionN+S), R(RightBundle Branch).
Highlights
The ECG signal is a representation of the electrical heart activity; it is used to analyze the status of the heart
Many researches were proposed for the automatic classification of the signal heart many methods have been implemented, such as the statistic approach, fuzzy logic and neural networks
At present, the basic QT is the only database that is annotated appropriately to test our analysis method of long-term recordings. It contains 105 two-channel Holter-recordings primarily (ITNs and V5) of 15 minutes sampled at 250 Hz. These ECG signals were extracted from different databases already existing such as the database arrhythmias MIT-BIH, the database ST-T of the European Society of Cardiology, and several other databases assembled by Boston's Beth medical center vectors
Summary
The ECG signal is a representation of the electrical heart activity; it is used to analyze the status of the heart. Due to the morphological variability of the different waves of ECG and the presence of noise that interfere with the ECG signal it is so difficult to extract necessary information’s from the signal. These difficulties require tools of signal processing and automatic classification of cardiac Arrhythmia. We chose to use the Kohonen topological maps [7,8,9] receives as input 12 parameters characterizing a temporal and morphological ECG beat are mainly: (Length (QRS), amp (Q, R,S) intervals (QT, PR, RR)).
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