Multimedia Tools and Applications

, Volume 77, Issue 3, pp 3775–3793 | Cite as

Sensorineural hearing loss detection via discrete wavelet transform and principal component analysis combined with generalized eigenvalue proximal support vector machine and Tikhonov regularization

  • Yi Chen
  • Ming Yang
  • Xianqing Chen
  • Bin Liu
  • Hainan Wang
  • Shuihua WangEmail author


In the past, scholars used various computer vision and artificial intelligence methods to detect brain diseases via magnetic resonance imaging (MRI). In this paper, we proposed a novel system to detect sensorineural hearing loss (SNHL). First, we used three-level bior4.4 wavelet to decompose original brain image. Second, principal component analysis (PCA) was utilized for dimensionality reduction. Third, the generalized eigenvalue proximal support vector machine (GEPSVM) with Tikhonov regularization was employed as the classifier. The 10 repetitions of five-fold cross validation showed our method achieved an overall accuracy of 95.71 %. Our sensitivities over healthy control, left-sided SNHL, and right-sided SNHL are 96.00 %, 95.33 %, and 95.71 %, respectively. The proposed system is promising and effective in SNHL detection. It gives better performance than four state-of-the-art methods.


Discrete wavelet transform Tikhonov regularization Sensorineural hearing loss Magnetic resonance imaging Dimensionality reduction 



This paper was supported by NSFC (61602250, 61503188, 61562041), Natural Science Foundation of Jiangsu Province (BK20150983, BK20150982), Program of Natural Science Research of Jiangsu Higher Education Institutions (14KJB520021), Open Research Fund of Hunan Provincial Key Laboratory of Network Investigational Technology (2016WLZC013), Open Fund of Fujian Provincial Key Laboratory of Data Intensive Computing (BD201607), Jiangsu Key Laboratory of Image and Video Understanding for Social Safety, Nanjing University of Science and Technology (30916014107).


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Yi Chen
    • 1
    • 2
    • 3
  • Ming Yang
    • 4
    • 5
  • Xianqing Chen
    • 6
  • Bin Liu
    • 7
  • Hainan Wang
    • 1
    • 3
    • 5
  • Shuihua Wang
    • 1
    • 8
    Email author
  1. 1.School of Computer Science and TechnologyNanjing Normal UniversityNanjingChina
  2. 2.Hunan Provincial Key Laboratory of Network Investigational TechnologyHunan Policy AcademyChangshaChina
  3. 3.Key Laboratory of Image and Video Understanding for Social SafetyNanjing University of Science and TechnologyNanjingChina
  4. 4.Department of Radiology, Nanjing Children’s HospitalNanjing Medical UniversityNanjingChina
  5. 5.Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial UniversityQuanzhou Normal UniversityQuanzhouChina
  6. 6.Department of electrical engineering, College of engineeringZhejiang Normal UniversityJinhuaChina
  7. 7.Department of RadiologyZhong-Da Hospital of Southeast UniversityNanjingChina
  8. 8.Department of Electrical EngineeringThe city college of New YorkNew YorkUSA

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