Diagnosis of hearing impairment based on wavelet transformation and machine learning approach
Hearing impairment has become the most widespread sensory disorder in the world, obstructing human-to-human communication and comprehension. The EEG-based brain-computer interface (BCI) technology may be an important solution to rehabilitating their hearing capacity for people who are unable to sust...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Springer Science and Business Media Deutschland GmbH
2022
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| Online Access: | https://umpir.ump.edu.my/id/eprint/39580/ |
| _version_ | 1848827303205797888 |
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| author | Islam, Md. Nahidul Norizam, Sulaiman Mahfuzah, Mustafa |
| author_facet | Islam, Md. Nahidul Norizam, Sulaiman Mahfuzah, Mustafa |
| author_sort | Islam, Md. Nahidul |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Hearing impairment has become the most widespread sensory disorder in the world, obstructing human-to-human communication and comprehension. The EEG-based brain-computer interface (BCI) technology may be an important solution to rehabilitating their hearing capacity for people who are unable to sustain verbal contact and behavioral response by sound stimulation. Auditory evoked potentials (AEPs) are a kind of EEG signal produced by an acoustic stimulus from the brain scalp. This study aims to develop an intelligent hearing level assessment technique using AEP signals to address these concerns. First, we convert the raw AEP signals into the time–frequency image using the continuous wavelet transform (CWT). Then, the Support vector machine (SVM) approach is used for classifying the time–frequency images. This study uses the reputed publicly available dataset to check the validation of the proposed approach. This approach achieves a maximum of 95.21% classification accuracy, which clearly indicates that the approach provides a very encouraging performance for detecting the AEPs responses in determining human auditory level. |
| first_indexed | 2025-11-15T03:34:46Z |
| format | Conference or Workshop Item |
| id | ump-39580 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:58:34Z |
| publishDate | 2022 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-395802025-09-04T01:41:41Z https://umpir.ump.edu.my/id/eprint/39580/ Diagnosis of hearing impairment based on wavelet transformation and machine learning approach Islam, Md. Nahidul Norizam, Sulaiman Mahfuzah, Mustafa T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Hearing impairment has become the most widespread sensory disorder in the world, obstructing human-to-human communication and comprehension. The EEG-based brain-computer interface (BCI) technology may be an important solution to rehabilitating their hearing capacity for people who are unable to sustain verbal contact and behavioral response by sound stimulation. Auditory evoked potentials (AEPs) are a kind of EEG signal produced by an acoustic stimulus from the brain scalp. This study aims to develop an intelligent hearing level assessment technique using AEP signals to address these concerns. First, we convert the raw AEP signals into the time–frequency image using the continuous wavelet transform (CWT). Then, the Support vector machine (SVM) approach is used for classifying the time–frequency images. This study uses the reputed publicly available dataset to check the validation of the proposed approach. This approach achieves a maximum of 95.21% classification accuracy, which clearly indicates that the approach provides a very encouraging performance for detecting the AEPs responses in determining human auditory level. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en https://umpir.ump.edu.my/id/eprint/39580/1/Diagnosis%20of%20Hearing%20Impairment%20Based%20on%20Wavelet%20Transformation.pdf pdf en https://umpir.ump.edu.my/id/eprint/39580/2/Diagnosis%20of%20hearing%20impairment%20based%20on%20wavelet%20transformation%20and%20machine%20learning%20approach_ABS.pdf Islam, Md. Nahidul and Norizam, Sulaiman and Mahfuzah, Mustafa (2022) Diagnosis of hearing impairment based on wavelet transformation and machine learning approach. In: Lecture Notes in Electrical Engineering; 6th International Conference on Electrical, Control and Computer Engineering, InECCE 2021 , 23 August 2021 , Kuantan, Pahang. pp. 705-715., 842 (274719). ISSN 1876-1100 ISBN 978-981168689-4 (Published) https://doi.org/10.1007/978-981-16-8690-0_62 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Islam, Md. Nahidul Norizam, Sulaiman Mahfuzah, Mustafa Diagnosis of hearing impairment based on wavelet transformation and machine learning approach |
| title | Diagnosis of hearing impairment based on wavelet transformation and machine learning approach |
| title_full | Diagnosis of hearing impairment based on wavelet transformation and machine learning approach |
| title_fullStr | Diagnosis of hearing impairment based on wavelet transformation and machine learning approach |
| title_full_unstemmed | Diagnosis of hearing impairment based on wavelet transformation and machine learning approach |
| title_short | Diagnosis of hearing impairment based on wavelet transformation and machine learning approach |
| title_sort | diagnosis of hearing impairment based on wavelet transformation and machine learning approach |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | https://umpir.ump.edu.my/id/eprint/39580/ https://umpir.ump.edu.my/id/eprint/39580/ |