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Open AccessArticle

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

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School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China
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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
*
Author to whom correspondence should be addressed.
These authors contribute equally to this paper.
Academic Editor: Carlo Cattani
Entropy 2016, 18(5), 194; https://doi.org/10.3390/e18050194
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)
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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MDPI and ACS Style

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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