Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ) as new algorithm with integrating feature extraction and classification for Arrhythmia heartbeats classification

Electrocardiogram (ECG) plays an important role in monitoring and preventing heart attacks. In this paper, we propose a new method Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ) that integrated feature extraction and classification for the automatic classification of heartbeat...

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Bibliographic Details
Published in:2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) pp. 150 - 155
Main Authors: Imah, E. M., Jatmiko, W., Basaruddin, T.
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2012
Subjects:
Accuracy
AMGLVQ
arrhythmia
back-propagation
Classification algorithms
ECG
Electrocardiography
Feature extraction
Heart beat
Support vector machines
SVM
Testing
ISBN:9781467317139, 1467317136
ISSN:1062-922X
Online Access:Get full text
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Summary:Electrocardiogram (ECG) plays an important role in monitoring and preventing heart attacks. In this paper, we propose a new method Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ) that integrated feature extraction and classification for the automatic classification of heartbeats in an ECG signal. Since this task has specific characteristics such as, inconsistency optimization on feature extraction and classification, unclassifiable beats and a strong class unbalance, so in this study we proposed new algorithm to handle the problems. The algorithm will be evaluated on real ECG signals from the MIT arrhythmia database. The Experiments show that the proposed method can improve the accuracy of classification better than SVM or back-propagation NN and also able to handle some problems of heartbeat classification: unbalance class, inconsistency between feature extraction and classification and detecting unknown beat on testing phase.
ISBN:9781467317139
1467317136
ISSN:1062-922X
DOI:10.1109/ICSMC.2012.6377692