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Entropy 2016, 18(5), 194; doi:10.3390/e18050194

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

1,†
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1,8,9,*
1
School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China
2
Department of Radiology, Nanjing Children’s Hospital, Nanjing Medical University, Nanjing 210008, China
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School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
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School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
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School of Information Science and Engineering, Changzhou University, Changzhou 213164, China
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Department of Radiology, Zhong Da Hospital, Southeast University, Nanjing 210009, China
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Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing 210042, China
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Key Laboratory of Statistical Information Technology and Data Mining, State Statistics Bureau, Chengdu 610225, China
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Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China
These authors contribute equally to this paper.
*
Author to whom correspondence should be addressed.
Academic Editor: Carlo Cattani
Received: 30 March 2016 / Revised: 12 May 2016 / Accepted: 16 May 2016 / Published: 19 May 2016
(This article belongs to the Special Issue Wavelets, Fractals and Information Theory II)
View Full-Text   |   Download PDF [1734 KB, uploaded 19 May 2016]   |  

Abstract

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. View Full-Text
Keywords: artificial neural network; fractional Fourier transform; Levenberg–Marquardt algorithm; principal component analysis; hearing loss; computer-aided diagnosis; unified time-frequency domain artificial neural network; fractional Fourier transform; Levenberg–Marquardt algorithm; principal component analysis; hearing loss; computer-aided diagnosis; unified time-frequency domain
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wang, S.; Yang, M.; Zhang, Y.; Li, J.; Zou, L.; Lu, S.; Liu, B.; Yang, J.; Zhang, Y. Detection of Left-Sided and Right-Sided Hearing Loss via Fractional Fourier Transform. Entropy 2016, 18, 194.

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