Algorithm for classifying arrhythmia using Extreme Learning Machine and principal component analysis.
In this paper, we developed the novel algorithm for cardiac arrhythmia classification. Until now, back propagation neural network (BPNN) was frequently used for these tasks. However, general gradient based learning method is far slower than what is required for their application. The proposed algorithm adapts Extreme Learning Machine(ELM) that has the advantage of very fast learning speed and high accuracy. In this paper, we classify beats into normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, atrial premature beat, paced beat, and ventricular escape beat. Experimental results show that we can obtain 97.45% in average accuracy, 97.44% in average sensitivity, 98.46% in average specificity, and 2.423 seconds in learning time.
Kim
Jinkwon
J
Department of Electrical and Electronic Engineering, Yonsei University, 134 Sinchon-dong, Seodaemun-gu, Seoul, Korea. jinkwon-mailbox@yonsei.ac.kr
Shin
Hangsik
H
Lee
Yonwook
Y
Lee
Myoungho
M
D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis* / Q000503:physiopathology*; D001185:Artificial Intelligence; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006339:Heart Rate; D006801:Humans; D008955:Models, Cardiovascular; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis; D012984:Software