Hearing Loss Detection Based on Wavelet Entropy and Genetic Algorithm

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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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  -