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.

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

Graphical Abstract