Open Access
Algorithms, volume 16, issue 5, pages 255
Neural Network Entropy (NNetEn): Entropy-Based EEG Signal and Chaotic Time Series Classification, Python Package for NNetEn Calculation
Velichko Andrey
1
,
Belyaev Maksim
1
,
Izotov Yuriy
1
,
Murugappan Murugappan
2, 3, 4
,
Heidari Hanif
5
Publication type: Journal Article
Publication date: 2023-05-16
Computational Mathematics
Computational Theory and Mathematics
Theoretical Computer Science
Numerical Analysis
Abstract
Entropy measures are effective features for time series classification problems. Traditional entropy measures, such as Shannon entropy, use probability distribution function. However, for the effective separation of time series, new entropy estimation methods are required to characterize the chaotic dynamic of the system. Our concept of Neural Network Entropy (NNetEn) is based on the classification of special datasets in relation to the entropy of the time series recorded in the reservoir of the neural network. NNetEn estimates the chaotic dynamics of time series in an original way and does not take into account probability distribution functions. We propose two new classification metrics: R2 Efficiency and Pearson Efficiency. The efficiency of NNetEn is verified on separation of two chaotic time series of sine mapping using dispersion analysis. For two close dynamic time series (r = 1.1918 and r = 1.2243), the F-ratio has reached the value of 124 and reflects high efficiency of the introduced method in classification problems. The electroencephalography signal classification for healthy persons and patients with Alzheimer disease illustrates the practical application of the NNetEn features. Our computations demonstrate the synergistic effect of increasing classification accuracy when applying traditional entropy measures and the NNetEn concept conjointly. An implementation of the algorithms in Python is presented.
Citations by journals
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Sensors
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Sensors
1 publication, 25%
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Space Weather
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Space Weather
1 publication, 25%
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BioSystems
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BioSystems
1 publication, 25%
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Mathematics
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Mathematics
1 publication, 25%
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1
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Citations by publishers
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2
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Multidisciplinary Digital Publishing Institute (MDPI)
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Multidisciplinary Digital Publishing Institute (MDPI)
2 publications, 50%
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Wiley
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Wiley
1 publication, 25%
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Elsevier
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Elsevier
1 publication, 25%
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1
2
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Velichko A. et al. Neural Network Entropy (NNetEn): Entropy-Based EEG Signal and Chaotic Time Series Classification, Python Package for NNetEn Calculation // Algorithms. 2023. Vol. 16. No. 5. p. 255.
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Velichko A., Belyaev M., Izotov Y., Murugappan M., Heidari H. Neural Network Entropy (NNetEn): Entropy-Based EEG Signal and Chaotic Time Series Classification, Python Package for NNetEn Calculation // Algorithms. 2023. Vol. 16. No. 5. p. 255.
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TY - JOUR
DO - 10.3390/a16050255
UR - https://doi.org/10.3390%2Fa16050255
TI - Neural Network Entropy (NNetEn): Entropy-Based EEG Signal and Chaotic Time Series Classification, Python Package for NNetEn Calculation
T2 - Algorithms
AU - Velichko, Andrey
AU - Belyaev, Maksim
AU - Izotov, Yuriy
AU - Murugappan, Murugappan
AU - Heidari, Hanif
PY - 2023
DA - 2023/05/16 00:00:00
PB - Multidisciplinary Digital Publishing Institute (MDPI)
SP - 255
IS - 5
VL - 16
SN - 1999-4893
ER -
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@article{2023_Velichko,
author = {Andrey Velichko and Maksim Belyaev and Yuriy Izotov and Murugappan Murugappan and Hanif Heidari},
title = {Neural Network Entropy (NNetEn): Entropy-Based EEG Signal and Chaotic Time Series Classification, Python Package for NNetEn Calculation},
journal = {Algorithms},
year = {2023},
volume = {16},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
month = {may},
url = {https://doi.org/10.3390%2Fa16050255},
number = {5},
pages = {255},
doi = {10.3390/a16050255}
}
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Velichko, Andrey, et al. “Neural Network Entropy (NNetEn): Entropy-Based EEG Signal and Chaotic Time Series Classification, Python Package for NNetEn Calculation.” Algorithms, vol. 16, no. 5, May. 2023, p. 255. https://doi.org/10.3390%2Fa16050255.
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