Abstract: Electro Cardiogram’s (ECG) biomedical signals characterizing cardiac anomalies are used for identifying cardiac arrhythmia. Irregular heartbeat B Arrhythmia B 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 study 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 B Feed Forward Neural Network (SA-FFNN). Experiments were conducted with the MIT-BIH arrhythmia database for evaluating the proposed methods.