Electrocardiogram signals identification for cardiac arrhythmias using prony's method and neural network.
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).
Bani-Hasan
Moustafa A
MA
Biomedical Engineering Department, Cairo University, Giza, Egypt.
Kadah
Yasser M
YM
Rasmy
Mohamed E M
ME
El-Hefnawi
Fatma M
FM
D000465:Algorithms; D001146:Arrhythmia, Sinus / Q000503:physiopathology; D001145:Arrhythmias, Cardiac / Q000175:diagnosis* / Q000503:physiopathology; D003198:Computer Simulation; D004562:Electrocardiography / Q000379:methods*; D006329:Heart Conduction System / Q000503:physiopathology; D006339:Heart Rate / Q000502:physiology; D006352:Heart Ventricles / Q000503:physiopathology; D006801:Humans; D008955:Models, Cardiovascular; D009415:Nerve Net; D009474:Neurons / Q000502:physiology; D014693:Ventricular Fibrillation / Q000175:diagnosis / Q000503:physiopathology