Auditory Evoked Potentials (AEPs) Response Classification: A Fast Fourier Transform (FFT) and Support Vector Machine (SVM) Approach
Hearing loss has become the world's most widespread sensory impairment. The applicability of a traditional hearing test is limited as it allows the subject to provide a direct response. The main aim of this study is to build an intelligent hearing level evaluation method using possible auditory...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33315/ http://umpir.ump.edu.my/id/eprint/33315/1/Auditory%20Evoked%20Potentials%20%28AEPs%29%20Response%20Classification%20A%20Fast%20Fourier%20Transform%20%28FFT%29%20and%20Support%20Vector%20Machine%20%28SVM%29%20Approach..pdf |
| _version_ | 1848824224470269952 |
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| author | Islam, Md Nahidul Norizam, Sulaiman Rashid, Mamunur Mahfuzah, Mustafa Md Jahid, Hasan |
| author_facet | Islam, Md Nahidul Norizam, Sulaiman Rashid, Mamunur Mahfuzah, Mustafa Md Jahid, Hasan |
| author_sort | Islam, Md Nahidul |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Hearing loss has become the world's most widespread sensory impairment. The applicability of a traditional hearing test is limited as it allows the subject to provide a direct response. The main aim of this study is to build an intelligent hearing level evaluation method using possible auditory evoked signals (AEPs). AEP responses are subjected to fixed acoustic stimulation strength for usual auditory and abnormal ear subjects to detect the hearing disorder. In this paper, the AEP responses have been captured from the sixteen subjects when the subject hears the auditory stimulus in the left or right ear. Then, the features have extracted with the help of Fast Fourier Transform (FFT), Power Spectral Density (PSD), Spectral Centroids, Standard Deviation algorithms. To classify the extracted features, the Support Vector Machine (SVM) approach using Radial Basis Kernel Function (RBF) has been used. Finally, the performance of the classifier in terms of accuracy, confusion matrix, true positive and false negative rate, precision, recall, and Cohen-Kappa-Score have been evaluated. The maximum classification accuracy of the developed SVM model with FFT feature was observed 95.29% (10 s time windows) which clearly indicates that the method provides a very encouraging performance for detecting the AEPs responses.. |
| first_indexed | 2025-11-15T03:09:38Z |
| format | Conference or Workshop Item |
| id | ump-33315 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:09:38Z |
| publishDate | 2022 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-333152022-02-07T03:28:23Z http://umpir.ump.edu.my/id/eprint/33315/ Auditory Evoked Potentials (AEPs) Response Classification: A Fast Fourier Transform (FFT) and Support Vector Machine (SVM) Approach Islam, Md Nahidul Norizam, Sulaiman Rashid, Mamunur Mahfuzah, Mustafa Md Jahid, Hasan TK Electrical engineering. Electronics Nuclear engineering Hearing loss has become the world's most widespread sensory impairment. The applicability of a traditional hearing test is limited as it allows the subject to provide a direct response. The main aim of this study is to build an intelligent hearing level evaluation method using possible auditory evoked signals (AEPs). AEP responses are subjected to fixed acoustic stimulation strength for usual auditory and abnormal ear subjects to detect the hearing disorder. In this paper, the AEP responses have been captured from the sixteen subjects when the subject hears the auditory stimulus in the left or right ear. Then, the features have extracted with the help of Fast Fourier Transform (FFT), Power Spectral Density (PSD), Spectral Centroids, Standard Deviation algorithms. To classify the extracted features, the Support Vector Machine (SVM) approach using Radial Basis Kernel Function (RBF) has been used. Finally, the performance of the classifier in terms of accuracy, confusion matrix, true positive and false negative rate, precision, recall, and Cohen-Kappa-Score have been evaluated. The maximum classification accuracy of the developed SVM model with FFT feature was observed 95.29% (10 s time windows) which clearly indicates that the method provides a very encouraging performance for detecting the AEPs responses.. 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/33315/1/Auditory%20Evoked%20Potentials%20%28AEPs%29%20Response%20Classification%20A%20Fast%20Fourier%20Transform%20%28FFT%29%20and%20Support%20Vector%20Machine%20%28SVM%29%20Approach..pdf Islam, Md Nahidul and Norizam, Sulaiman and Rashid, Mamunur and Mahfuzah, Mustafa and Md Jahid, Hasan (2022) Auditory Evoked Potentials (AEPs) Response Classification: A Fast Fourier Transform (FFT) and Support Vector Machine (SVM) Approach. In: Proceedings of the 12th National Technical Seminar on Unmanned System Technology 2020: NUSYS’20 , 27-28 October 2020 , Virtually via the IEEE OES Malaysia Virtual/Online Conference Platform. pp. 539-549., 770. (Published) https://doi.org/10.1007/978-981-16-2406-3_41 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Islam, Md Nahidul Norizam, Sulaiman Rashid, Mamunur Mahfuzah, Mustafa Md Jahid, Hasan Auditory Evoked Potentials (AEPs) Response Classification: A Fast Fourier Transform (FFT) and Support Vector Machine (SVM) Approach |
| title | Auditory Evoked Potentials (AEPs) Response Classification: A Fast Fourier Transform (FFT) and Support Vector Machine (SVM) Approach |
| title_full | Auditory Evoked Potentials (AEPs) Response Classification: A Fast Fourier Transform (FFT) and Support Vector Machine (SVM) Approach |
| title_fullStr | Auditory Evoked Potentials (AEPs) Response Classification: A Fast Fourier Transform (FFT) and Support Vector Machine (SVM) Approach |
| title_full_unstemmed | Auditory Evoked Potentials (AEPs) Response Classification: A Fast Fourier Transform (FFT) and Support Vector Machine (SVM) Approach |
| title_short | Auditory Evoked Potentials (AEPs) Response Classification: A Fast Fourier Transform (FFT) and Support Vector Machine (SVM) Approach |
| title_sort | auditory evoked potentials (aeps) response classification: a fast fourier transform (fft) and support vector machine (svm) approach |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/33315/ http://umpir.ump.edu.my/id/eprint/33315/ http://umpir.ump.edu.my/id/eprint/33315/1/Auditory%20Evoked%20Potentials%20%28AEPs%29%20Response%20Classification%20A%20Fast%20Fourier%20Transform%20%28FFT%29%20and%20Support%20Vector%20Machine%20%28SVM%29%20Approach..pdf |