Abstract
(Aim) Unilateral sensorineural hearing loss is a brain disease, which causes slight morphology within brain structure. Traditional manual method can ignore this change. (Method) First, we used dual-tree complex wavelet transform to extract features. Afterwards, we used kernel principal component analysis to reduce feature dimensionalities. Finally, multinomial logistic regression was employed to be the classifier. (Result) The 10 times of 10-fold stratified cross validation showed our method achieved an overall accuracy of 96.17 \(\mathrm {\pm }\) 2.49%. The sensitivities of detecting left-sided sensorineural hearing loss, right-sided sensorineural hearing loss, and healthy controls were 96.00 \(\mathrm {\pm }\) 2.58%, 96.50 \(\mathrm {\pm }\) 2.42%, and 96.00 \(\mathrm {\pm }\) 3.16%, respectively. (Conclusion) Our method performed better than five state-of-the-art methods.
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Acknowledgment
This paper was supported by Leading Initiative for Excellent Young Researcher (LEADER) of Ministry of Education, Culture, Sports, Science and Technology-Japan (16809746), Natural Science Foundation of China (61602250, 61503188, 61562041, 61271231), Natural Science Foundation of Jiangsu Province (BK20150983, BK20150982), Program of Natural Science Research of Jiangsu Higher Education Institutions (16KJB520025, 15KJB470010), Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing (BM2013006), Open fund for Jiangsu Key Laboratory of Advanced Manufacturing Technology (HGAMTL1601), Open Program of Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing (3DL201602).
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Wang, S., Zhang, Y., Yang, M., Liu, B., Ramirez, J., Gorriz, J.M. (2017). Preliminary Study on Unilateral Sensorineural Hearing Loss Identification via Dual-Tree Complex Wavelet Transform and Multinomial Logistic Regression. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Natural and Artificial Computation for Biomedicine and Neuroscience. IWINAC 2017. Lecture Notes in Computer Science(), vol 10337. Springer, Cham. https://doi.org/10.1007/978-3-319-59740-9_28
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