<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Electrocardiogram (ECG) signal for human identity recognition is a new area on biometrics research. The ECG is a vital signal of human body, unique, robustness to attack, universality and permanence, difference to others traditional biometrics technic. This study also proposes Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ), that integrating feature extraction and classification method. The experiments shown that AMGLVQ can improve the accuracy of classification better than SVM or back-propagation NN and also able to handle some problems of heartbeat classification: imbalanced data set, inconsistency between feature extraction and classification and detecting unknown data on testing phase.</p></abstract>

Electrocardiogram (ECG) signal for human identity recognition is a new area on biometrics research. The ECG is a vital signal of human body, unique, robustness to attack, universality and permanence, difference to others traditional biometrics technic. This study also proposes Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ), that integrating feature extraction and classification method. The experiments shown that AMGLVQ can improve the accuracy of classification better than SVM or back-propagation NN and also able to handle some problems of heartbeat classification: imbalanced data set, inconsistency between feature extraction and classification and detecting unknown data on testing phase.

Electrocardiogram for Biometrics by using Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ): Integrating Feature Extraction and Classification

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Electrocardiogram (ECG) signal for human identity recognition is a new area on biometrics research. The ECG is a vital signal of human body, unique, robustness...

Electrocardiogram for Biometrics by using Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ): Integrating Feature Extraction and Classification

Published Online: Dec 16, 2013

Page range: 1891 - 1917

Received: Jul 03, 2013

Accepted: Oct 28, 2013

DOI: https://doi.org/10.21307/ijssis-2017-619

© 2013 Elly Matul Imah et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.