Proceedings of the 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017)

Hearing Loss Detection Based on Wavelet Entropy and Genetic Algorithm

Authors
Fangyuan Liu, Arifur Nayeem, Atiena Pereira
Corresponding Author
Fangyuan Liu
Available Online November 2017.
DOI
10.2991/amms-17.2017.11How to use a DOI?
Keywords
hearing loss; wavelet entropy; feedforward neural network; genetic algorithm
Abstract

In order to develop a new hearing loss detection method, this paper proposed to combine wavelet entropy with feedforward neural network trained by genetic algorithm. The dataset contains 72 subjects—24 healthy controls, 24 left-sided hearing loss patients, and 24 right-sided hearing loss patients. The 10 runs of 8-fold cross validation showed that optimal decomposition level was 4, better than the results using decomposition level of 2, 3, and 5. Our method using 4-level decomposition yielded a sensitivity for healthy controls of 81.25±4.91%, a sensitivity for left-sided hearing loss of 80.42±5.57%, a sensitivity for right-sided hearing loss of 81.67±6.86%, and an overall accuracy of 81.11±1.34%.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017)
Series
Advances in Intelligent Systems Research
Publication Date
November 2017
ISBN
978-94-6252-433-0
ISSN
1951-6851
DOI
10.2991/amms-17.2017.11How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Fangyuan Liu
AU  - Arifur Nayeem
AU  - Atiena Pereira
PY  - 2017/11
DA  - 2017/11
TI  - Hearing Loss Detection Based on Wavelet Entropy and Genetic Algorithm
BT  - Proceedings of the 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017)
PB  - Atlantis Press
SP  - 49
EP  - 53
SN  - 1951-6851
UR  - https://doi.org/10.2991/amms-17.2017.11
DO  - 10.2991/amms-17.2017.11
ID  - Liu2017/11
ER  -