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Unilateral sensorineural hearing loss identification based on double-density dual-tree complex wavelet transform and multinomial logistic regression

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journal contribution
posted on 26.03.2020, 15:36 by Shui-Hua Wang, Yu-Dong Zhang, Ming Yang, Bin Liu, Javier Ramirez, Juan M. Gorriz
AIM: Unilateral sensorineural hearing loss is a brain disease, which causes slight morphology changes within brain structure. Traditional manual method may ignore this change. METHOD: In this work, we developed a novel method, based on the double-density dual-tree complex (DDDTCWT), and radial basis function kernel principal component analysis (RKPCA) and multinomial logistic regression (MLR) for the magnetic resonance imaging scanning. We first used DDDTCWT to extract features. Afterwards, we used RKPCA to reduce feature dimensionalities. Finally, MLR was employed to be the classifier. RESULT: The 10 times of 10-fold stratified cross validation showed our method achieved an overall accuracy of 96.44 ± 0.88%. The sensitivities of detecting left-sided sensorineural hearing loss, right-sided sensorineural hearing loss, and healthy controls were 96.67 ± 2.72%, 96.67 ± 3.51%, and 96.00 ± 4.10%, respectively. CONCLUSION: Our method performed better than both raw and improved AlexNet, and eight state-of-the-art methods via a stringent statistical 10 × 10-fold stratified cross validation. The MLR gives better classification performance than decision tree, support vector machine, and back-propagation neural network.

History

Citation

Integrated Computer-Aided Engineering, vol. 26, no. 4, pp. 411-426, 2019

Author affiliation

Department of Informatics

Version

AM (Accepted Manuscript)

Published in

INTEGRATED COMPUTER-AIDED ENGINEERING

Volume

26

Issue

4

Pagination

411 - 426

Publisher

IOS PRESS

issn

1069-2509

eissn

1875-8835

Copyright date

2019

Available date

11/09/2019

Publisher version

https://content.iospress.com/articles/integrated-computer-aided-engineering/ica190605

Language

English