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Fuzzy learning vector quantization particle swarm optimization (FLVQ-PSO) and fuzzy neuro generalized learning vector quantization (FN-GLVQ) for automatic early detection system of heart diseases based on real-time electrocardiogram

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4 Author(s)
Rachmadi, M.F. ; Fac. of Comput. Sci., Univ. Indonesia, Depok, Indonesia ; Ma'sum, M.A. ; Setiawan, I.M.A. ; Jatmiko, W.

Automatic heart beats classification has attracted much interest for research recently and we are interested to determine the type of arrhythmia from electrocardiogram (ECG) signal automatically. This paper will discuss thoroughly about study and implementation of FLVQ-PSO, an extension from FLVQ algorithm which use MSA and PSO method, and FN-GLVQ, an extension from GLVQ algorithm which use fuzzy logic concept, to classify ECG signals. By using 10-Fold Cross Validation, the algorithm produced an average accuracy 84.02%, 98.25%, 99.00%, and 97.70%, respectively for FLVQ, FLVQ-PSO, GLVQ, and FN-GLVQ.

Published in:

SICE Annual Conference (SICE), 2012 Proceedings of

Date of Conference:

20-23 Aug. 2012