Detection of unilateral hearing loss by stationary wavelet entropy

Yudong Zhang, Deepak Ranjan Nayak, Ming Yang, Ti Fei Yuan, Bin Liu, Huimin Lu, Shuihua Wang*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

37 Citations (Scopus)


Aim: Sensorineural hearing loss is correlated to massive neurological or psychiatric disease. Materials: T1-weighted volumetric images were acquired from fourteen subjects with right-sided hearing loss (RHL), fifteen subjects with left-sided hearing loss (LHL), and twenty healthy controls (HC). Method: We treated a three-class classification problem: HC, LHL, and RHL. Stationary wavelet entropy was employed to extract global features from magnetic resonance images of each subject. Those stationary wavelet entropy features were used as input to a single-hidden layer feedforward neural-network classifier. Results: The 10 repetition results of 10-fold cross validation show that the accuracies of HC, LHL, and RHL are 96.94%, 97.14%, and 97.35%, respectively. Conclusion: Our developed system is promising and effective in detecting hearing loss.

Original languageEnglish
Pages (from-to)122-128
Number of pages7
JournalCNS and Neurological Disorders - Drug Targets
Issue number2
Publication statusPublished - 2017
Externally publishedYes


  • Computer-aided diagnosis
  • Sensorineural hearing loss
  • Single-hidden layer feed forward neural-network
  • Stationary wavelet entropy
  • Unilateral hearing loss


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