1Faculty of Science, University of Zakho, Kurdistan Region, Iraq
2College of Science, International University of Erbil, Kurdistan Region, Iraq
3Department of Cardiology, Azadi Teaching Hospital, Duhok, Kurdistan Region, Iraq
*Corresponding e-mail: alan.ahmad@uoz.edu.krd
Online published on 19 November, 2018.
This study is based on classifying the ECG signal into five types of classes by using statistical and timing intervals features. First, the data signals were denoised and prepared for classification. Second, 24 higher order statistical features with 3 timing interval features were extracted from each selected beat. In this work, we have 5 types of classes, atrial premature contractions (APC), normal (NOR), premature ventricular contractions (PVC), left bundle branch block (LBBB) and right bundle branch block (RBBB) were used for classification. Third, each beat was classified according to one of these classes by using the learner algorithm scaled conjugate gradient (SCG) artificial neural network (ANN). SCG is a fast algorithm and suitable in cases of less memory and ANN is a machine learning algorithm that is based on the biological neural system. The experimental results of this work show an accuracy of 96% on 1400 beats taken from 14 records from MIT/BIH arrhythmia database.
ECG, Higher order statistics, Feature extraction, Classification, ANN, Scaled conjugate gradient
