Paper Info
Title
Preliminary Study on Unilateral Sensorineural Hearing Loss Identification via Dual-Tree Complex Wavelet Transform and Multinomial Logistic Regression.
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 +/- 2.49%. The sensitivities of detecting left-sided sensorineural hearing loss, right-sided sensorineural hearing loss, and healthy controls were 96.00 +/- 2.58%, 96.50 +/- 2.42%, and 96.00 +/- 3.16%, respectively. (Conclusion) Our method performed better than five state-of-the-art methods.
Year
DOI
Venue
2017
10.1007/978-3-319-59740-9_28
Lecture Notes in Computer Science
Keywords
Field
DocType
Unilateral sensorineural hearing loss,Dual-tree complex wavelet transform,Kernel principal component analysis,Multinomial logistic regression,Magnetic resonance imaging
Pattern recognition,Sensorineural hearing loss,Multinomial logistic regression,Computer science,Dual tree,Speech recognition,Kernel principal component analysis,Artificial intelligence,Complex wavelet transform,Classifier (linguistics),Cross-validation,Machine learning
Conference
Volume
ISSN
Citations 
10337
0302-9743
3
PageRank 
References 
Authors
0.37
10
6
Name
Order
Citations
PageRank
Shuihua Wang1156487.49
yudong zhang2133490.44
Ming Yang324226.75
Bin Liu419135.02
Javier Ramírez565668.23
J. M. Górriz657054.40