Abstract
Hearing deficiency is the world’s most common sensation of impairment and impedes human communication and learning. One of the best ways to solve this problem is early and successful hearing diagnosis using electroencephalogram (EEG). Auditory Evoked Potential (AEP) seems to be a form of EEG signal with an auditory stimulus produced from the cortex of the brain. This study aims to develop an intelligent system of auditory sensation to analyze and evaluate the functional reliability of the hearing to solve these problems based on the AEP response. We create deep learning frameworks to enhance the training process of the deep neural network in order to achieve highly accurate hearing deficit diagnoses. In this study, a publicly available AEP dataset has been used and the responses have been obtained from the five subjects when the subject hears the auditory stimulus in the left or right ear. First, through a wavelet transformation, the raw AEP data is transformed into time-frequency images. Then, to remove lower-level functionality, a pre-trained network is used. Then the labeled images of time-frequency are then used to fine-tune the neural network architecture’s higher levels. On this AEP dataset, we have achieved 92.7% accuracy. The proposed deep CNN architecture provides better outcomes with fewer learnable parameters for hearing loss diagnosis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Deafness and hearing loss. https://www.who.int/news-room/fact-sheets/detail/deafness-and-hearing-loss. Accessed 18 June 2020
Zhang, Y., Zhang, Y., Wang, J., Zheng, X.: Comparison of classification methods on EEG signals based on wavelet packet decomposition. Neural Comput. Appl. 26(5), 1217–1225 (2014). https://doi.org/10.1007/s00521-014-1786-7
Five-Class SSVEP Response Detection using Common-Spatial Pattern (CSP)-SVM Approach | Int. J. Integr. Eng. https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/6580. Accessed 05 Dec 2020
Gao, S., Wang, Y., Gao, X.: Visual and auditory brain – computer interfaces. 61, 1436–1447 (2014)
Alam, S., Technology, E., Bari, B.S., Technology, E., Jadin, M.S., Technology, E.: Empirical mode decomposition coupled with fast fourier transform based feature extraction method for motor imagery tasks classification, pp. 10–15 (2020)
Sriraam, N.: EEG based automated detection of auditory loss: a pilot study. Expert Syst. Appl. 39, 723–731 (2012). https://doi.org/10.1016/j.eswa.2011.07.064
Ibrahim, I.A., Ting, H.-N., Moghavvemi, M.: Formulation of a novel classification indices for classification of human hearing abilities according to cortical auditory event potential signals. Arab. J. Sci. Eng. 44(8), 7133–7147 (2019). https://doi.org/10.1007/s13369-019-03835-5
Dietl, H., Weiss, S.: Detection of cochlear hearing loss applying wavelet packets and support vector machines. In: Conference Record - Asilomar Conference on Signals, Systems and Computers, pp. 1575–1579 (2004). https://doi.org/10.1109/acssc.2004.1399421
Tang, C., Lee, E.: Hearing loss identification via wavelet entropy and combination of Tabu search and particle swarm optimization. In: International Conference on Digital Signal Processing, DSP. Institute of Electrical and Electronics Engineers Inc. (2019). https://doi.org/10.1109/ICDSP.2018.8631839.
Sanjay, H.S., Hiremath, B.V., Prithvi, B.S., Dinesh, P.A.: Machine learning based assessment of auditory threshold perception in human beings. SN Appl. Sci. 2(2), 1–10 (2020). https://doi.org/10.1007/s42452-019-1929-7
Xue, P., Bai, J., Wang, Q., Zhang, X., Feng, P.: Analysis and classification of the nasal finals in hearing-impaired patients using tongue movement features. Speech Commun. 104, 57–65 (2018). https://doi.org/10.1016/j.specom.2018.09.008
Zhang, R., McAllister, G., Scotney, B., McClean, S., Houston, G.: Combining wavelet analysis and Bayesian networks for the classification of auditory brainstem response. IEEE Trans. Inf. Technol. Biomed. 10, 458–467 (2006). https://doi.org/10.1109/TITB.2005.863865
Li, P.Z., Huang, L., Wang, C.D., Li, C., Lai, J.H.: Brain network analysis for auditory disease: a twofold study. Neurocomputing 347, 230–239 (2019). https://doi.org/10.1016/j.neucom.2019.04.013
Yan, R., Gao, R.X., Chen, X.: Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Process. 96, 1–15 (2014). https://doi.org/10.1016/j.sigpro.2013.04.015
Huang, L., Wang, J.: Forecasting energy fluctuation model by wavelet decomposition and stochastic recurrent wavelet neural network. Neurocomputing 309, 70–82 (2018). https://doi.org/10.1016/j.neucom.2018.04.071
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE. 86, 2278–2323 (1998). https://doi.org/10.1109/5.726791
Ravi, D., et al.: Deep learning for Health Informatics. IEEE J. Biomed. Heal. Informatics. 21, 4–21 (2017). https://doi.org/10.1109/JBHI.2016.2636665
Nielsen, M.: Neural networks and deep learning
Earliest Diabetic Retinopathy Classification Using Deep Convolution Neural Networks.pdf | Request PDF. https://www.researchgate.net/publication/309260065_Earliest_Diabetic_Retinopathy_Classification_Using_Deep_Convolution_Neural_Networkspdf. Accessed 30 May 2020
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: OverFeat: integrated recognition, localization and detection using convolutional networks (2013)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. International Conference on Learning Representations, ICLR (2015)
Acknowledgment
The author would like to acknowledge the magnificent supports from the Faculty of Electrical & Electronics Engineering Technology and Universiti Malaysia Pahang and Ministry of Education Malaysia to provide fundamental research grant scheme to support this research, FRGS/1/2018/TK04/UMP/02/3 (RDU190109).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Islam, M.N., Sulaiman, N., Rashid, M., Hasan, M.J., Mustafa, M., P. P. Abdul Majeed, A. (2021). Analysis of Auditory Evoked Potential Signals Using Wavelet Transform and Deep Learning Techniques. In: Chew, E., et al. RiTA 2020. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-16-4803-8_39
Download citation
DOI: https://doi.org/10.1007/978-981-16-4803-8_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-4802-1
Online ISBN: 978-981-16-4803-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)