چکیده (انگلیسی):

Electro Cardiogram’s (ECG) biomedical signals characterizing cardiac anomalies are used for identifying cardiac
arrhythmia. Irregular heartbeat-Arrhythmia-affects heart rate causing problems. Many methods, trying to simplify
arrhythmia monitoring through automated detection, were developed over the years. ECG classification for
arrhythmia is investigated in this paper based on soft computing techniques. RR interval are extracted from time
series of the ECG and used as feature for arrhythmia classification. Frequency domain extracted features are
classified using Radial Basis Function (RBF) and proposed Spline Activated-Feed Forward Neural Network (SAFFNN).
Experiments were conducted with the Massachusetts Institute of Technology-Boston’s Beth Israel
Hospital (MIT-BIH) arrhythmia database for evaluating the proposed methods.