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

Wang, Shuihua; Zhang, Yudong; Yang, Ming; Liu, Bin; Ramı́rez, Javier; Górriz, Juan Manuel

doi:10.1007/978-3-319-59740-9_28

Cited by 6 publications

(8 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, based on the above characteristics, scholars generally choose to use computer vision combined with machine learning to detect hearing loss [4][5][6][7][8], and previous experiments have also shown the effectiveness of this system. But many existing detection systems easily fall into the dilemma of local optimization when training and optimizing neural networks.…”

Section: Snhl Brings Language Communication and Evenmentioning

confidence: 99%

Hearing loss classification via stationary wavelet entropy and Biogeography-based optimization

Yao¹,

Tan²,

Sun³

2020

EAI Endorsed Transactions on e-Learning

View full text Add to dashboard Cite

Introduction: Sensorineural hearing loss is associated with many complications and needs timely detection and diagnosis. Objectives: Optimize the sensorineural hearing loss detection system to improve the accuracies of image detection. Method: The stationary wavelet entropy was used to extract the features of NMR images, the single hidden layer neural network was used for classification, and the BBO algorithm was used for optimization to avoid the dilemma of local optimum. We used two-level SWE as input to the classifier to enhance the identify and classify ability of hearing loss. Results: The results of 10-fold cross validation show that the accuracies of HC, LHL and RHL are 91.83± 3.09%, 92.67±2.38% and 91.17±2.61%, respectively. The overall accuracy is 91.89±0.70%. Conclusion: This model has good performance in detecting hearing loss.

show abstract

Section: Snhl Brings Language Communication and Evenmentioning

confidence: 99%

Hearing loss classification via stationary wavelet entropy and Biogeography-based optimization

Yao¹,

Tan²,

Sun³

2020

EAI Endorsed Transactions on e-Learning

View full text Add to dashboard Cite

show abstract

“…Later, Wang et al used the discrete wavelet transform [ 4 ] and dual-tree wavelet transform [ 5 ] to extract entropy feature. Then the overall accuracy to classify reached to 95.31% and 96.17 ± 2.49% respectively.…”

Section: Introductionmentioning

confidence: 99%

Hearing Loss Identification via Fractional Fourier Entropy and Direct Acyclic Graph Support Vector Machine

Wang

2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

With the risk of hearing loss being higher than before since the digital device is more popular, it becomes more urgent to identify the sensorineural hearing loss from the view of changes in internal brain structure. Based on 180 brain MRI of three categories of hearing loss balanced dataset, one schema with fractional Fourier transform entropy and direct acyclic graph support vector machine is proposed and applied to identify the features and predict the categories of hearing loss. The experiments prove this schema rather promising when the dataset is not large since the overall accuracy is up to 94.06 ± 1.08% which is higher than those of some previous methods in scope of traditional machine learning.

show abstract

“…To solve these issues, Wang, Zhang (21), our previous work in IWINAC 2017, enrolled more USHL patients to balance the dataset. Besides, they proposed to combine dual-tree complex wavelet transform (DTCWT) and kernel principal component analysis to reduce the features.…”

Section: Introductionmentioning

confidence: 99%

“…This paper is an extension of Wang, Zhang (21). The new extensions include following eleven points: (i) We increase the 60-subject dataset to 90-subject.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Zhang

Yang

et al. 2019

ICA

Self Cite

View full text Add to dashboard Cite

Aim) Unilateral sensorineural hearing loss is a brain disease, which causes slight morphology change 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 use 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 fine-tuned AlexNet, and eight state-of-the-art methods via a stringent statistical 10x10fold stratified cross validation. The MLR gives better classification performance than decision tree, support vector machine, and back-propagation neural network.

show abstract

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

Cited by 6 publications

References 28 publications

Hearing loss classification via stationary wavelet entropy and Biogeography-based optimization

Hearing loss classification via stationary wavelet entropy and Biogeography-based optimization

Hearing Loss Identification via Fractional Fourier Entropy and Direct Acyclic Graph Support Vector Machine

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

Contact Info

Product

Resources

About