Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Neural Networks

ISNN 2017: Advances in Neural Networks - ISNN 2017 pp 541–549Cite as

Hearing Loss Detection in Medical Multimedia Data by Discrete Wavelet Packet Entropy and Single-Hidden Layer Neural Network Trained by Adaptive Learning-Rate Back Propagation

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10262))

Abstract

In order to develop an efficient computer-aided diagnosis system for detecting left-sided and right-sided sensorineural hearing loss, we used artificial intelligence in this study. First, 49 subjects were enrolled by magnetic resonance imaging scans. Second, the discrete wavelet packet entropy (DWPE) was utilized to extract global texture features from brain images. Third, single-hidden layer neural network (SLNN) was used as the classifier with training algorithm of adaptive learning-rate back propagation (ALBP). The 10 times of 5-fold cross validation demonstrated our proposed method yielded an overall accuracy of 95.31%, higher than standard back propagation method with accuracy of 87.14%. Besides, our method also outperforms the “FRFT + PCA (Yang, 2016)”, “WE + DT (Kale, 2013)”, and “WE + MRF (Vasta 2016)”. In closing, our method is efficient.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Nakagawa, T., Yamamoto, M., Kumakawa, K., Usami, S., Hato, N., Tabuchi, K., Takahashi, M., Fujiwara, K., Sasaki, A., Komune, S., Yamamoto, N., Hiraumi, H., Sakamoto, T., Shimizu, A., Ito, J.: Prognostic impact of salvage treatment on hearing recovery in patients with sudden sensorineural hearing loss refractory to systemic corticosteroids: a retrospective observational study. Auris Nasus Larynx 43, 489–494 (2016)

    Article  Google Scholar 

  2. Prasad, A., Ghosh, P.K.: Information theoretic optimal vocal tract region selection from real time magnetic resonance images for broad phonetic class recognition. Comput. Speech Lang. 39, 108–128 (2016)

    Article  Google Scholar 

  3. Sun, P.: Preliminary research on abnormal brain detection by wavelet-energy and quantum-behaved PSO. Technol. Health Care 24, S641–S649 (2016)

    Article  Google Scholar 

  4. Pattanaworapan, K., Chamnongthai, K., Guo, J.M.: Signer-independence finger alphabet recognition using discrete wavelet transform and area level run lengths. J. Vis. Commun. Image Represent. 38, 658–677 (2016)

    Article  Google Scholar 

  5. Dong, Z., Phillips, P., Ji, G., Yang, J.: Exponential wavelet iterative shrinkage thresholding algorithm for compressed sensing magnetic resonance imaging. Inf. Sci. 322, 115–132 (2015)

    Article  MathSciNet  Google Scholar 

  6. Mao, Y., Ma, M.F., Tian, X.: Phase Synchronization Analysis of theta-band of Local Field Potentials in the Anterior Cingulated Cortex of Rats under Fear Conditioning. In: International Symposium on Intelligent Information Technology Application, pp. 737–741. IEEE Computer Society (2008)

    Google Scholar 

  7. Ikawa, N.: Automated averaging of auditory evoked response waveforms using wavelet analysis, Int. J. Wavelets Multiresolut. Inf. Process. 11 (2013). Article ID: 1360009

    Google Scholar 

  8. Nayak, D.R., Dash, R., Majhi, B.: Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing 177, 188–197 (2016)

    Article  Google Scholar 

  9. Lahmiri, S.: Image characterization by fractal descriptors in variational mode decomposition domain: application to brain magnetic resonance. Phys. A 456, 235–243 (2016)

    Article  MathSciNet  Google Scholar 

  10. Chen, Y., Chen, X.-Q.: Sensorineural hearing loss detection via discrete wavelet transform and principal component analysis combined with generalized eigenvalue proximal support vector machine and Tikhonov regularization. Multimedia Tools Appl. (2016). doi:10.1007/s11042-016-4087-6

  11. Gorriz, J.M., Ramírez, J.: Wavelet entropy and directed acyclic graph support vector machine for detection of patients with unilateral hearing loss in MRI scanning, Frontiers Comput. Neurosci. 10 (2016) Article ID: 160

    Google Scholar 

  12. Liu, A.: Magnetic resonance brain image classification via stationary wavelet transform and generalized eigenvalue proximal support vector machine. J. Med. Imaging Health Inform. 5, 1395–1403 (2015)

    Article  Google Scholar 

  13. Zhou, X.-X., Yang, J.-F., Sheng, H., Wei, L., Yan, J., Sun, P.: Combination of stationary wavelet transform and kernel support vector machines for pathological brain detection. Simulation 92, 827–837 (2016)

    Article  Google Scholar 

  14. Ghods, A., Lee, H.H.: Probabilistic frequency-domain discrete wavelet transform for better detection of bearing faults in induction motors. Neurocomputing 188, 206–216 (2016)

    Article  Google Scholar 

  15. Sun, Y.X., Zhuang, C.G., Xiong, Z.H.: Real-time chatter detection using the weighted wavelet packet entropy. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 1652–1657. IEEE, New York (2014)

    Google Scholar 

  16. Vyas, B., Maheshwari, R.P., Das, B.: Investigation for improved artificial intelligence techniques for thyristor-controlled series-compensated transmission line fault classification with discrete wavelet packet entropy measures. Electr. Power Compon. Syst. 42, 554–566 (2014)

    Article  Google Scholar 

  17. Yang, J.: Identification of green, oolong and black teas in China via wavelet packet entropy and fuzzy support vector machine. Entropy 17, 6663–6682 (2015)

    Article  Google Scholar 

  18. Arrais, E., Valentim, R.A.M., Brandao, G.B.: Real time QRS detection based on redundant discrete wavelet transform. IEEE Lat. Am. Trans. 14, 1662–1668 (2016)

    Article  Google Scholar 

  19. Hamzah, F.A.B., Yoshida, T., Iwahashi, M., Kiya, H.: Adaptive directional lifting structure of three dimensional non-separable discrete wavelet transform for high resolution volumetric data compression. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. E99A, 892–899 (2016)

    Article  Google Scholar 

  20. Kumar, A., Pooja, R., Singh, G.K.: Performance of different window functions for designing quadrature mirror filter bank using closed form method. Int. J. Signal Imaging Syst. Eng. 8, 367–379 (2015)

    Article  Google Scholar 

  21. Zhang, Y.D., Dong, Z.C., Ji, G.L., Wang, S.H.: An improved reconstruction method for CS-MRI based on exponential wavelet transform and iterative shrinkage/thresholding algorithm. J. Electromagn. Waves Appl. 28, 2327–2338 (2014)

    Article  Google Scholar 

  22. Baranwal, N., Singh, N., Nandi, G.C.: Indian sign language gesture recognition using discrete wavelet packet transform. In: International Conference on Signal Propagation and Computer Technology, pp. 573–577. IEEE (2014)

    Google Scholar 

  23. Gokmen, G.: The defect detection in glass materials by using discrete wavelet packet transform and artificial neural network. J. Vibroengineering 16, 1434–1443 (2014)

    Google Scholar 

  24. Qin, Z.J., Wang, N., Gao, Y., Cuthbert, L.: Adaptive threshold for energy detector based on discrete wavelet packet transform. In: Wireless Telecommunications Symposium, pp. 171–177. IEEE (2012)

    Google Scholar 

  25. Ghafourian, M., Hassanabadi, H.: Shannon information entropies for the three-dimensional Klein-Gordon problem with the Poschl-Teller potential. J. Korean Phys. Soc. 68, 1267–1271 (2016)

    Article  Google Scholar 

  26. Phillips, P., Dong, Z., Yang, J.: Pathological brain detection in magnetic resonance imaging scanning by wavelet entropy and hybridization of biogeography-based optimization and particle swarm optimization. Prog. Electromagnet. Res. 152, 41–58 (2015)

    Article  Google Scholar 

  27. Alcoba, D.R., Torre, A., Lain, L., Massaccesi, G.E., Ona, O.B., Ayers, P.W., Van Raemdonck, M., Bultinck, P., Van Neck, D.: Performance of Shannon-entropy compacted N-electron wave functions for configuration interaction methods. Theor. Chem. Acc. 135(11), 153 (2016)

    Article  Google Scholar 

  28. Zhan, T.M., Chen, Y.: Multiple sclerosis detection based on biorthogonal wavelet transform, RBF kernel principal component analysis, and logistic regression. IEEE Access 4, 7567–7576 (2016)

    Article  MathSciNet  Google Scholar 

  29. Du, S.: Alzheimer’s disease detection by Pseudo Zernike moment and linear regression classification. CNS Neurol. Disord. - Drug Targets 16, 11–15 (2017)

    Article  Google Scholar 

  30. Chen, Y.: A feature-free 30-disease pathological brain detection system by linear regression classifier. CNS Neurol. Disord. - Drug Targets 16, 5–10 (2017)

    Article  Google Scholar 

  31. Lu, S., Qiu, X.: A pathological brain detection system based on extreme learning machine optimized by bat algorithm. CNS Neurol. Disord. - Drug Targets 16, 23–29 (2017)

    Article  Google Scholar 

  32. Zhou, X.-X.: Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine. Simulation 92, 861–871 (2016)

    Article  Google Scholar 

  33. Zhang, Y.: Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowl.-Based Syst. 64, 22–31 (2014)

    Article  Google Scholar 

  34. Abbas, H.A., Belkheiri, M., Zegnini, B.: Feedback linearisation control of an induction machine augmented by single-hidden layer neural networks. Int. J. Control 89, 140–155 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  35. Sun, Y.: A multilayer perceptron based smart pathological brain detection system by fractional fourier entropy. J. Med. Syst. 40, 173 (2016)

    Article  Google Scholar 

  36. Hicham, A., Mohamed, B., Abdellah, E.F.: A model for sales forecasting based on fuzzy clustering and back-propagation neural networks with adaptive learning rate. In: International Conference on Complex Systems, pp. 111–115. IEEE (2012)

    Google Scholar 

  37. Lu, H.M.: Facial emotion recognition based on biorthogonal wavelet entropy, fuzzy support vector machine, and stratified cross validation. IEEE Access 4, 8375–8385 (2016)

    Article  Google Scholar 

  38. Iranmanesh, S.: A diffferential adaptive learning rate method for back-propagation neural networks. In: Proceedings of the 10th Wseas International Conference on Neural Networks, pp. 30–34. World Scientific And Engineering Acad And Soc (2009)

    Google Scholar 

  39. Murru, N., Rossini, R.: A Bayesian approach for initialization of weights in backpropagation neural net with application to character recognition. Neurocomputing 193, 92–105 (2016)

    Article  Google Scholar 

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

    Article  Google Scholar 

  41. Kale, M.C., Fleig, J.D., Imal, N.: Assessment of feasibility to use computer aided texture analysis based tool for parametric images of suspicious lesions in DCE-MR mammography, Comput. Math. Method Med. (2013). Article ID: 872676

    Google Scholar 

  42. Vasta, R., Augimeri, A., Cerasa, A., Nigro, S., Gramigna, V., Nonnis, M., Rocca, F., Zito, G., Quattrone, A.: ADNI: hippocampal subfield atrophies in converted and not-converted mild cognitive impairments patients by a markov random fields algorithm. Curr. Alzheimer Res. 13, 566–574 (2016)

    Article  Google Scholar 

  43. Hou, X.: Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed. Multimedia Tools Appl. (2017). doi:10.1007/s11042-017-4554-8

  44. Nogueira, R.F., Lotufo, R.D., Machado, R.C.: Fingerprint liveness detection using convolutional neural networks. IEEE Trans. Inf. Forensic Secur. 11, 1206–1213 (2016)

    Article  Google Scholar 

  45. Chen, M., Li, Y., Han, L.: Detection of dendritic spines using wavelet-based conditional symmetric analysis and regularized morphological shared-weight neural networks, Comput. Math. Method Med. (2015). Article ID: 454076

    Google Scholar 

Download references

Acknowledgment

This study is supported by NSFC (61602250, 61271231), Program of Natural Science Research of Jiangsu Higher Education Institutions (16KJB520025, 15KJB470010), Natural Science Foundation of Jiangsu Province (BK20150983), Leading Initiative for Excellent Young Researcher (LEADER) of Ministry of Education, Culture, Sports, Science and Technology, Japan (16809746), Open Program of Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing (3DL201602), Open fund of Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence (2016CSCI01), Open fund of Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence (2016CSCI01).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing Normal University, Nanjing, 210023, Jiangsu, China

    Shuihua Wang & Yudong Zhang

  2. School of Electronic Science and Engineering, Nanjing University, Nanjing, 210046, Jiangsu, China

    Sidan Du & Yang Li

  3. Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Fukuoka Prefecture, 804-8550, Japan

    Huimin Lu

  4. Department of Radiology, Children’s Hospital of Nanjing Medical University, Nanjing, 210008, China

    Ming Yang

  5. Department of Radiology, Zhong-Da Hospital of Southeast University, Nanjing, 210009, China

    Bin Liu

  6. Translational Imaging Division, Columbia University, New York, NY, 10032, USA

    Yudong Zhang

Authors
  1. Shuihua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sidan Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huimin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ming Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yudong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yudong Zhang .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fengyu Cong

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Andrew Leung

  3. Chinese Academy of Sciences, Beijing, China

    Qinglai Wei

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Wang, S. et al. (2017). Hearing Loss Detection in Medical Multimedia Data by Discrete Wavelet Packet Entropy and Single-Hidden Layer Neural Network Trained by Adaptive Learning-Rate Back Propagation. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_63

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59081-3_63

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59080-6

  • Online ISBN: 978-3-319-59081-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation