Detection of Unilateral Hearing Loss by Stationary Wavelet Entropy

Title:Detection of Unilateral Hearing Loss by Stationary Wavelet Entropy

Volume: 16 Issue: 2

Author(s): Yudong Zhang, Deepak Ranjan Nayak, Ming Yang, Ti-Fei Yuan, Bin Liu, Huimin Lu and Shuihua Wang

Affiliation:

Keywords: Computer-aided diagnosis, sensorineural hearing loss, single-hidden layer feed forward neural-network, stationary wavelet entropy, unilateral hearing loss.

Abstract: 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 neuralnetwork 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.

Cite this article as:

Zhang Yudong, Nayak Ranjan Deepak, Yang Ming, Yuan Ti-Fei, Liu Bin, Lu Huimin and Wang Shuihua, Detection of Unilateral Hearing Loss by Stationary Wavelet Entropy, CNS & Neurological Disorders - Drug Targets 2017; 16 (2) . https://dx.doi.org/10.2174/1871527315666161026115046

DOI https://dx.doi.org/10.2174/1871527315666161026115046	Print ISSN 1871-5273
Publisher Name Bentham Science Publisher	Online ISSN 1996-3181