Multiple neural network integration using a binary decision tree to improve the ECG signal recognition accuracy

Hoai Linh Tran; Van Nam Pham; Hoang Nam Vuong

International Journal of Applied Mathematics and Computer Science (2014)

  • Volume: 24, Issue: 3, page 647-655
  • ISSN: 1641-876X

Abstract

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The paper presents a new system for ECG (ElectroCardioGraphy) signal recognition using different neural classifiers and a binary decision tree to provide one more processing stage to give the final recognition result. As the base classifiers, the three classical neural models, i.e., the MLP (Multi Layer Perceptron), modified TSK (Takagi-Sugeno-Kang) and the SVM (Support Vector Machine), will be applied. The coefficients in ECG signal decomposition using Hermite basis functions and the peak-to-peak periods of the ECG signals will be used as features for the classifiers. Numerical experiments will be performed for the recognition of different types of arrhythmia in the ECG signals taken from the MIT-BIH (Massachusetts Institute of Technology and Boston's Beth Israel Hospital) Arrhythmia Database. The results will be compared with individual base classifiers' performances and with other integration methods to show the high quality of the proposed solution.

How to cite

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Hoai Linh Tran, Van Nam Pham, and Hoang Nam Vuong. "Multiple neural network integration using a binary decision tree to improve the ECG signal recognition accuracy." International Journal of Applied Mathematics and Computer Science 24.3 (2014): 647-655. <http://eudml.org/doc/271895>.

@article{HoaiLinhTran2014,
abstract = {The paper presents a new system for ECG (ElectroCardioGraphy) signal recognition using different neural classifiers and a binary decision tree to provide one more processing stage to give the final recognition result. As the base classifiers, the three classical neural models, i.e., the MLP (Multi Layer Perceptron), modified TSK (Takagi-Sugeno-Kang) and the SVM (Support Vector Machine), will be applied. The coefficients in ECG signal decomposition using Hermite basis functions and the peak-to-peak periods of the ECG signals will be used as features for the classifiers. Numerical experiments will be performed for the recognition of different types of arrhythmia in the ECG signals taken from the MIT-BIH (Massachusetts Institute of Technology and Boston's Beth Israel Hospital) Arrhythmia Database. The results will be compared with individual base classifiers' performances and with other integration methods to show the high quality of the proposed solution.},
author = {Hoai Linh Tran, Van Nam Pham, Hoang Nam Vuong},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {neural classifiers; integration of classifiers; decision tree; arrhythmia recognition; Hermite basis function decomposition},
language = {eng},
number = {3},
pages = {647-655},
title = {Multiple neural network integration using a binary decision tree to improve the ECG signal recognition accuracy},
url = {http://eudml.org/doc/271895},
volume = {24},
year = {2014},
}

TY - JOUR
AU - Hoai Linh Tran
AU - Van Nam Pham
AU - Hoang Nam Vuong
TI - Multiple neural network integration using a binary decision tree to improve the ECG signal recognition accuracy
JO - International Journal of Applied Mathematics and Computer Science
PY - 2014
VL - 24
IS - 3
SP - 647
EP - 655
AB - The paper presents a new system for ECG (ElectroCardioGraphy) signal recognition using different neural classifiers and a binary decision tree to provide one more processing stage to give the final recognition result. As the base classifiers, the three classical neural models, i.e., the MLP (Multi Layer Perceptron), modified TSK (Takagi-Sugeno-Kang) and the SVM (Support Vector Machine), will be applied. The coefficients in ECG signal decomposition using Hermite basis functions and the peak-to-peak periods of the ECG signals will be used as features for the classifiers. Numerical experiments will be performed for the recognition of different types of arrhythmia in the ECG signals taken from the MIT-BIH (Massachusetts Institute of Technology and Boston's Beth Israel Hospital) Arrhythmia Database. The results will be compared with individual base classifiers' performances and with other integration methods to show the high quality of the proposed solution.
LA - eng
KW - neural classifiers; integration of classifiers; decision tree; arrhythmia recognition; Hermite basis function decomposition
UR - http://eudml.org/doc/271895
ER -

References

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