Preliminary Study on Unilateral Sensorineural Hearing Loss Identification via Dual-Tree Complex Wavelet Transform and Multinomial Logistic Regression-Reference-Cited by-同舟云学术

Preliminary Study on Unilateral Sensorineural Hearing Loss Identification via Dual-Tree Complex Wavelet Transform and Multinomial Logistic Regression

Published:2017 Issue: Volume: Page:289-297
ISSN:0302-9743
Container-title:Natural and Artificial Computation for Biomedicine and Neuroscience
language:
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Author:

Wang Shuihua,Zhang Yudong,Yang Ming,Liu Bin,Ramirez Javier,Gorriz Juan Manuel

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-59740-9_28

Reference31 articles.

1. Vos, T., et al.: Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the global burden of disease study 2013. Lancet 386, 743–800 (2015)

2. Parker, M.A.: Biotechnology in the treatment of sensorineural hearing loss: foundations and future of hair cell regeneration. J. Speech Lang. Hear. Res. 54, 1709–1731 (2011)

3. Yang, M., et al.: Brain structural and functional alterations in patients with unilateral hearing loss. Hear. Res. 316, 37–43 (2014)

4. Li, J.: Detection of left-sided and right-sided hearing loss via fractional fourier transform. Entropy 18 (2016). Article ID: 194

5. Chen, P.: Computer-aided detection of left and right sensorineural hearing loss by wavelet packet decomposition and least-square support vector machine. J. Am. Geriatr. Soc. 64 (2016). Article ID: S350

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