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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...

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Main Authors: Islam, Md. Nahidul, Norizam, Sulaiman, Mahfuzah, Mustafa
Format: Conference or Workshop Item
Language:English
English
Published: Springer Science and Business Media Deutschland GmbH 2022
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/39580/1/Diagnosis%20of%20Hearing%20Impairment%20Based%20on%20Wavelet%20Transformation.pdf
http://umpir.ump.edu.my/id/eprint/39580/2/Diagnosis%20of%20hearing%20impairment%20based%20on%20wavelet%20transformation%20and%20machine%20learning%20approach_ABS.pdf
http://umpir.ump.edu.my/id/eprint/39580/
https://doi.org/10.1007/978-981-16-8690-0_62
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spelling my.ump.umpir.395802023-12-11T03:28:35Z http://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 http://umpir.ump.edu.my/id/eprint/39580/1/Diagnosis%20of%20Hearing%20Impairment%20Based%20on%20Wavelet%20Transformation.pdf pdf en http://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 https://doi.org/10.1007/978-981-16-8690-0_62
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
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
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.
format Conference or Workshop Item
author Islam, Md. Nahidul
Norizam, Sulaiman
Mahfuzah, Mustafa
author_facet Islam, Md. Nahidul
Norizam, Sulaiman
Mahfuzah, Mustafa
author_sort Islam, Md. Nahidul
title 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_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_sort diagnosis of hearing impairment based on wavelet transformation and machine learning approach
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/39580/1/Diagnosis%20of%20Hearing%20Impairment%20Based%20on%20Wavelet%20Transformation.pdf
http://umpir.ump.edu.my/id/eprint/39580/2/Diagnosis%20of%20hearing%20impairment%20based%20on%20wavelet%20transformation%20and%20machine%20learning%20approach_ABS.pdf
http://umpir.ump.edu.my/id/eprint/39580/
https://doi.org/10.1007/978-981-16-8690-0_62
_version_ 1822923973908758528
score 13.232414

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