{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T05:51:53Z","timestamp":1774158713609,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2016,5,19]],"date-time":"2016-05-19T00:00:00Z","timestamp":1463616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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\u2013Marquardt 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.<\/jats:p>","DOI":"10.3390\/e18050194","type":"journal-article","created":{"date-parts":[[2016,5,19]],"date-time":"2016-05-19T20:43:57Z","timestamp":1463690637000},"page":"194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Detection of Left-Sided and Right-Sided Hearing Loss via Fractional Fourier Transform"],"prefix":"10.3390","volume":"18","author":[{"given":"Shuihua","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China"}]},{"given":"Ming","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Nanjing Children\u2019s Hospital, Nanjing Medical University, Nanjing 210008, China"}]},{"given":"Yin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China"}]},{"given":"Jianwu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Ling","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Changzhou University, Changzhou 213164, China"}]},{"given":"Siyuan","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China"}]},{"given":"Bin","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Zhong Da Hospital, Southeast University, Nanjing 210009, China"}]},{"given":"Jiquan","family":"Yang","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing 210042, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4870-1493","authenticated-orcid":false,"given":"Yudong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China"},{"name":"Key Laboratory of Statistical Information Technology and Data Mining, State Statistics Bureau, Chengdu 610225, China"},{"name":"Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.jad.2016.03.020","article-title":"Risk of depressive disorders following sudden sensorineural hearing loss: A nationwide population-based retrospective cohort study","volume":"197","author":"Tseng","year":"2016","journal-title":"J. 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