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

• Published in: Integrated computer-aided engineering Vol. 26; no. 4; pp. 411 - 426
• Main Authors: Wang, Shui-Hua, Zhang, Yu-Dong, Yang, Ming, Liu, Bin, Ramirez, Javier, Gorriz, Juan Manuel
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2019; Sage Publications Ltd
• Subjects: Back propagation networks; Brain; Decision trees; Density; Feature extraction; Hearing loss; Hearing protection; Magnetic resonance imaging; Morphology; Neural networks; Principal components analysis; Radial basis function; Regression analysis; Statistical analysis; Statistical methods; Support vector machines; Wavelet transforms; multinomial logistic regression; alexNet; magnetic resonance imaging; Unilateral sensorineural hearing loss; double-density dual-tree complex wavelet transform; kernel principal component analysis; dual-tree complex wavelet transform
• ISSN: 1069-2509; 1875-8835
• DOI: 10.3233/ICA-190605

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.