Analysis of auditory evoked potential signals using wavelet transform and deep learning techniques
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...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Springer
2020
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/33314/ |
| _version_ | 1848827297392492544 |
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| author | Islam, Md Nahidul Norizam, Sulaiman Rashid, Mamunur Hasan, Md Jahid Mahfuzah, Mustafa Anwar P. P., Abdul Majeed |
| author_facet | Islam, Md Nahidul Norizam, Sulaiman Rashid, Mamunur Hasan, Md Jahid Mahfuzah, Mustafa Anwar P. P., Abdul Majeed |
| author_sort | Islam, Md Nahidul |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:09:38Z |
| format | Conference or Workshop Item |
| id | ump-33314 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:58:28Z |
| publishDate | 2020 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-333142025-09-04T01:33:15Z https://umpir.ump.edu.my/id/eprint/33314/ Analysis of auditory evoked potential signals using wavelet transform and deep learning techniques Islam, Md Nahidul Norizam, Sulaiman Rashid, Mamunur Hasan, Md Jahid Mahfuzah, Mustafa Anwar P. P., Abdul Majeed TK Electrical engineering. Electronics Nuclear engineering 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. Springer 2020 Conference or Workshop Item PeerReviewed pdf en https://umpir.ump.edu.my/id/eprint/33314/1/AnalysisofAuditoryEvokedPotentialSignals.pdf pdf en https://umpir.ump.edu.my/id/eprint/33314/2/AnalysisofAuditoryEvokedPotentialSignals1.pdf Islam, Md Nahidul and Norizam, Sulaiman and Rashid, Mamunur and Hasan, Md Jahid and Mahfuzah, Mustafa and Anwar P. P., Abdul Majeed (2020) Analysis of auditory evoked potential signals using wavelet transform and deep learning techniques. In: RiTA 2020: Proceedings of the 8th International Conference on Robot Intelligence Technology and Applications , 11-13 December 2020 , Virtual hosted by EUREKA Robotics Lab, Cardiff School of Technologies, Cardiff Metropolitan University. pp. 396-408.. ISBN 978-981-16-4803-8 (Published) https://doi.org/10.1007/978-981-16-4803-8_39 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Islam, Md Nahidul Norizam, Sulaiman Rashid, Mamunur Hasan, Md Jahid Mahfuzah, Mustafa Anwar P. P., Abdul Majeed Analysis of auditory evoked potential signals using wavelet transform and deep learning techniques |
| title | Analysis of auditory evoked potential signals using wavelet transform and deep learning techniques |
| title_full | Analysis of auditory evoked potential signals using wavelet transform and deep learning techniques |
| title_fullStr | Analysis of auditory evoked potential signals using wavelet transform and deep learning techniques |
| title_full_unstemmed | Analysis of auditory evoked potential signals using wavelet transform and deep learning techniques |
| title_short | Analysis of auditory evoked potential signals using wavelet transform and deep learning techniques |
| title_sort | analysis of auditory evoked potential signals using wavelet transform and deep learning techniques |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | https://umpir.ump.edu.my/id/eprint/33314/ https://umpir.ump.edu.my/id/eprint/33314/ |