Automatic classification of heartbeats using wavelet neural network.
The electrocardiogram (ECG) signal is widely employed as one of the most important tools in clinical practice in order to assess the cardiac status of patients. The classification of the ECG into different pathologic disease categories is a complex pattern recognition task. In this paper, we propose a method for ECG heartbeat pattern recognition using wavelet neural network (WNN). To achieve this objective, an algorithm for QRS detection is first implemented, then a WNN Classifier is developed. The experimental results obtained by testing the proposed approach on ECG data from the MIT-BIH arrhythmia database demonstrate the efficiency of such an approach when compared with other methods existing in the literature.
Benali
Radhwane
R
Department of Electronics, Abou Bekr Belkaid University, Tlemcen, Algeria. benali_redouane@yahoo.fr
Bereksi Reguig
Fethi
F
Hadj Slimane
Zinedine
Z
D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000145:classification* / Q000175:diagnosis*; D004562:Electrocardiography; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D058067:Wavelet Analysis