COMPLEXITY ANALYSIS OF MASS SPECTROMETRY DATA FOR DISEASE CLASSIFICATION USING GA-BASED MULTISCALE ENTROPY

Cuong C. To, Tuan D. Pham

Abstract

Entropy methods including approximate entropy (ApEn), sample entropy (SampEn) and multiscale entropy (MSE) have recently been applied to measure the complexity of finite length time series for classification of diseases. In order to effectively use these entropy methods, parameters such as m, r, and scale factor (in MSE) are to be determined. So far, there have been no general rules to select these parameters as they depend on particular problems. In this paper, we introduce a genetic algorithm (GA) based method for optimal selection of these parameters in a sense that the entropic difference between healthy and pathologic groups are maximized.

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Paper Citation


in Harvard Style

C. To C. and D. Pham T. (2011). COMPLEXITY ANALYSIS OF MASS SPECTROMETRY DATA FOR DISEASE CLASSIFICATION USING GA-BASED MULTISCALE ENTROPY . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011) ISBN 978-989-8425-35-5, pages 5-14. DOI: 10.5220/0003119800050014


in Bibtex Style

@conference{biosignals11,
author={Cuong C. To and Tuan D. Pham},
title={COMPLEXITY ANALYSIS OF MASS SPECTROMETRY DATA FOR DISEASE CLASSIFICATION USING GA-BASED MULTISCALE ENTROPY},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)},
year={2011},
pages={5-14},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003119800050014},
isbn={978-989-8425-35-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2011)
TI - COMPLEXITY ANALYSIS OF MASS SPECTROMETRY DATA FOR DISEASE CLASSIFICATION USING GA-BASED MULTISCALE ENTROPY
SN - 978-989-8425-35-5
AU - C. To C.
AU - D. Pham T.
PY - 2011
SP - 5
EP - 14
DO - 10.5220/0003119800050014