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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Veröffentlicht in:2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) S. 150 - 155
Hauptverfasser: Imah, E. M., Jatmiko, W., Basaruddin, T.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.10.2012
Schlagworte:
Accuracy
AMGLVQ
arrhythmia
back-propagation
Classification algorithms
ECG
Electrocardiography
Feature extraction
Heart beat
Support vector machines
SVM
Testing
ISBN:9781467317139, 1467317136
ISSN:1062-922X
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Abstract 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.
AbstractList 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.
Author Basaruddin, T.
Imah, E. M.
Jatmiko, W.
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  givenname: W.
  surname: Jatmiko
  fullname: Jatmiko, W.
  email: wisnuj@cs.ui.ac.id
  organization: Fac. of Comput. Sci., Univ. Indonesia, Depok, Indonesia
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  givenname: T.
  surname: Basaruddin
  fullname: Basaruddin, T.
  organization: Fac. of Comput. Sci., Univ. Indonesia, Depok, Indonesia
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Snippet Electrocardiogram (ECG) plays an important role in monitoring and preventing heart attacks. In this paper, we propose a new method Adaptive Multilayer...
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StartPage 150
SubjectTerms Accuracy
AMGLVQ
arrhythmia
back-propagation
Classification algorithms
ECG
Electrocardiography
Feature extraction
Heart beat
Support vector machines
SVM
Testing
Title Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ) as new algorithm with integrating feature extraction and classification for Arrhythmia heartbeats classification
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