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Detection of Left-Sided and Right-Sided Hearing Loss via Fractional Fourier Transform

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posted on 2019-08-19, 08:50 authored by S Wang, M Yang, Y Zhang, J Li, L Zou, S Lu, B Liu, J Yang
In order to detect hearing loss more efficiently and accurately, this study proposed a new method based on fractional Fourier transform (FRFT). Three-dimensional volumetric magnetic resonance images were obtained from 15 patients with left-sided hearing loss (LHL), 20 healthy controls (HC), and 14 patients with right-sided hearing loss (RHL). Twenty-five FRFT spectrums were reduced by principal component analysis with thresholds of 90%, 95%, and 98%, respectively. The classifier is the single-hidden-layer feed-forward neural network (SFN) trained by the Levenberg–Marquardt algorithm. The results showed that the accuracies of all three classes are higher than 95%. In all, our method is promising and may raise interest from other researchers.

Funding

This paper was supported by the Natural Science Foundation of Jiangsu Province (BK20150983), the Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing (BM2013006), the Program of Natural Science Research of Jiangsu Higher Education Institutions (15KJB470010), the Special Funds for Scientific and Technological Achievement Transformation Project in Jiangsu Province (BA2013058), the Nanjing Normal University Research Foundation for Talented Scholars (2013119XGQ0061, 2014119XGQ0080), the Open Project Program of the State Key Lab of CAD&CG, Zhejiang University (A1616), the Open Fund of Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Jilin University (93K172016K17), the Open Fund of Key Laboratory of Statistical Information Technology and Data Mining, the State Statistics Bureau (SDL201608), the Science and Technology Program of Changzhou City (CE20145055), and the Qing Lan Project of Jiangsu Province. We also thank Y. Chen for his substantial help.

History

Citation

Entropy, 2016, 18(5), 194

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Informatics

Version

  • VoR (Version of Record)

Published in

Entropy

Publisher

MDPI

issn

1099-4300

Acceptance date

2016-05-16

Copyright date

2016

Available date

2019-08-19

Publisher DOI

Publisher version

https://www.mdpi.com/1099-4300/18/5/194

Language

en

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    Keywords

    Science & TechnologyPhysical SciencesPhysics, MultidisciplinaryPhysicsartificial neural networkfractional Fourier transformLevenberg-Marquardt algorithmprincipal component analysishearing losscomputer-aided diagnosisunified time-frequency domainDISCRETE WAVELET TRANSFORMFEEDFORWARD NEURAL-NETWORKSUPPORT VECTOR MACHINETRANSMISSION-LINECLASSIFICATIONENTROPYIDENTIFICATIONALGORITHMIMAGES

    Licence

    CC BY 4.0

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