TY - GEN
T1 - Preliminary study on unilateral sensorineural hearing loss identification via dual-tree complex wavelet transform and multinomial logistic regression
AU - Wang, Shuihua
AU - Zhang, Yudong
AU - Yang, Ming
AU - Liu, Bin
AU - Ramirez, Javier
AU - Gorriz, Juan Manuel
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - (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.
AB - (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.
KW - Dual-tree complex wavelet transform
KW - Kernel principal component analysis
KW - Magnetic resonance imaging
KW - Multinomial logistic regression
KW - Unilateral sensorineural hearing loss
UR - http://www.scopus.com/inward/record.url?scp=85027024534&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-59740-9_28
DO - 10.1007/978-3-319-59740-9_28
M3 - Conference Proceeding
AN - SCOPUS:85027024534
SN - 9783319597393
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 289
EP - 297
BT - Natural and Artificial Computation for Biomedicine and Neuroscience - International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017, Proceedings
A2 - Adeli, Hojjat
A2 - Ferrandez Vicente, Jose Manuel
A2 - Toledo Moreo, Javier
A2 - Alvarez-Sanchez, Jose Ramon
A2 - de la Paz Lopez, Felix
PB - Springer Verlag
T2 - 7th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2017
Y2 - 19 June 2017 through 23 June 2017
ER -