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Electrocardiogram signals identification for cardiac arrhythmias using prony's method and neural network

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4 Author(s)
Bani-Hasan, M.A. ; Biomed. Eng. Dept., Cairo Univ., Cairo, Egypt ; Kadah, Y.M. ; Rasmy, M. ; El-Hefnawi, F.M.

A new method is presented to identify Electrocardiogram (ECG) signals for abnormal heartbeats based on Prony's modeling algorithm and neural network. Hence, the ECG signals can be written as a finite sum of exponential depending on poles. Neural network is used to identify the ECG signal from the calculated poles. Algorithm classification including a multi-layer feed forward neural network using back propagation is proposed as a classifying model to categorize the beats into one of five types including normal sinus rhythm (NSR), ventricular couplet (VC), ventricular tachycardia (VT), ventricular bigeminy (VB), and ventricular fibrillation (VF).

Published in:

Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE

Date of Conference:

3-6 Sept. 2009