Hearing disorder detection using auditory evoked potential (AEP) signals
Hearing deficit diagnoses is an important part of the audiological evaluation. The hearing disorder impairs human communication and learning. A traditional hearing test is constrained in its application and time-consuming since it requires the person to respond directly. The main objective of this s...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2020
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| Online Access: | http://umpir.ump.edu.my/id/eprint/31051/ http://umpir.ump.edu.my/id/eprint/31051/1/Hearing%20disorder%20detection%20using%20auditory%20evoked%20potential%20.pdf http://umpir.ump.edu.my/id/eprint/31051/2/Hearing%20disorder%20detection%20using%20auditory%20evoked%20potential_FULL.pdf |
| _version_ | 1848823669130788864 |
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| author | Islam, Md Nahidul Norizam, Sulaiman Rashid, Mamunur Bari, Bifta Sama Mahfuzah, Mustafa |
| author_facet | Islam, Md Nahidul Norizam, Sulaiman Rashid, Mamunur Bari, Bifta Sama Mahfuzah, Mustafa |
| author_sort | Islam, Md Nahidul |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Hearing deficit diagnoses is an important part of the audiological evaluation. The hearing disorder impairs human communication and learning. A traditional hearing test is constrained in its application and time-consuming since it requires the person to respond directly. The main objective of this study is to build an intelligent hearing level evaluation approach using Auditory Evoked Potential (AEPs) to address these concerns. For this purpose, two types of AEP signals (hearing auditory stimulus and hearing nothing) have been collected from five subjects with normal hearing abilities. Ten different statistical features have been extracted in ten different time window length (one second to ten seconds). The obtained feature sets have been classified by the K-Nearest Neighbors (K-NN) algorithm. Different types of the parameter of K-NN have been investigated also to achieve the best outcome. Experimental results show that the maximum classification accuracy of 97.80% has been achieved with the standard deviation feature and K-NN classification algorithm (Distance: Manhattan, K-neighbors: 4, Leaf size: 1, weight: uniform). The obtained performance indicates that the proposed method is very encouraging for diagnosing the AEPs responses. |
| first_indexed | 2025-11-15T03:00:48Z |
| format | Conference or Workshop Item |
| id | ump-31051 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:00:48Z |
| publishDate | 2020 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-310512022-02-04T06:54:45Z http://umpir.ump.edu.my/id/eprint/31051/ Hearing disorder detection using auditory evoked potential (AEP) signals Islam, Md Nahidul Norizam, Sulaiman Rashid, Mamunur Bari, Bifta Sama Mahfuzah, Mustafa RC Internal medicine TK Electrical engineering. Electronics Nuclear engineering Hearing deficit diagnoses is an important part of the audiological evaluation. The hearing disorder impairs human communication and learning. A traditional hearing test is constrained in its application and time-consuming since it requires the person to respond directly. The main objective of this study is to build an intelligent hearing level evaluation approach using Auditory Evoked Potential (AEPs) to address these concerns. For this purpose, two types of AEP signals (hearing auditory stimulus and hearing nothing) have been collected from five subjects with normal hearing abilities. Ten different statistical features have been extracted in ten different time window length (one second to ten seconds). The obtained feature sets have been classified by the K-Nearest Neighbors (K-NN) algorithm. Different types of the parameter of K-NN have been investigated also to achieve the best outcome. Experimental results show that the maximum classification accuracy of 97.80% has been achieved with the standard deviation feature and K-NN classification algorithm (Distance: Manhattan, K-neighbors: 4, Leaf size: 1, weight: uniform). The obtained performance indicates that the proposed method is very encouraging for diagnosing the AEPs responses. IEEE 2020-12-21 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/31051/1/Hearing%20disorder%20detection%20using%20auditory%20evoked%20potential%20.pdf pdf en http://umpir.ump.edu.my/id/eprint/31051/2/Hearing%20disorder%20detection%20using%20auditory%20evoked%20potential_FULL.pdf Islam, Md Nahidul and Norizam, Sulaiman and Rashid, Mamunur and Bari, Bifta Sama and Mahfuzah, Mustafa (2020) Hearing disorder detection using auditory evoked potential (AEP) signals. In: IEEE International Conference on Emerging Technology in Computing, Communication and Electronics (ETCCE 2020) , 21-22 December 2020 , Bangladesh. pp. 1-2. (9350918). ISBN 9780738124018 (Published) https://ieeexplore.ieee.org/document/9350918 |
| spellingShingle | RC Internal medicine TK Electrical engineering. Electronics Nuclear engineering Islam, Md Nahidul Norizam, Sulaiman Rashid, Mamunur Bari, Bifta Sama Mahfuzah, Mustafa Hearing disorder detection using auditory evoked potential (AEP) signals |
| title | Hearing disorder detection using auditory evoked potential (AEP) signals |
| title_full | Hearing disorder detection using auditory evoked potential (AEP) signals |
| title_fullStr | Hearing disorder detection using auditory evoked potential (AEP) signals |
| title_full_unstemmed | Hearing disorder detection using auditory evoked potential (AEP) signals |
| title_short | Hearing disorder detection using auditory evoked potential (AEP) signals |
| title_sort | hearing disorder detection using auditory evoked potential (aep) signals |
| topic | RC Internal medicine TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/31051/ http://umpir.ump.edu.my/id/eprint/31051/ http://umpir.ump.edu.my/id/eprint/31051/1/Hearing%20disorder%20detection%20using%20auditory%20evoked%20potential%20.pdf http://umpir.ump.edu.my/id/eprint/31051/2/Hearing%20disorder%20detection%20using%20auditory%20evoked%20potential_FULL.pdf |