TY  - JOUR
T1  - Spline Activated Neural Network for Classifying Cardiac Arrhythmia
AU - Kumar, R. Ganesh AU - Kumaraswamy, Y.S. 
JO  - International Journal of Soft Computing
VL  - 9
IS  - 6
SP  - 377
EP  - 385
PY  - 2014
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2014.377.385
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2014.377.385
KW  - ECG
KW  -arrhythmia classification
KW  -RR interval
KW  -feed forward neural network
KW  -multilayer perceptron
AB  - 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.
ER  -