Classification of EEG-based auditory evoked potentials using entropy-based features and machine learning techniques
Hearing loss is a prevalent impairment that disrupts interactions with others and individuals' learning abilities. Immediate and accurate diagnosis of hearing loss using Electroencephalogram (EEG) signals, particularly Auditory Evoked Potentials (AEP), is considered the most effective approach...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Institute of Electrical and Electronics Engineers Inc.
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/40384/1/Classification%20of%20EEG-based%20auditory%20evoked%20potentials.pdf http://umpir.ump.edu.my/id/eprint/40384/2/Classification%20of%20EEG-based%20auditory%20evoked%20potentials%20using%20entropy-based%20features%20and%20machine%20learning%20techniques_ABS.pdf |
| _version_ | 1796996244496711680 |
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| author | Islam, Thamina Ahmed, Firoz Ahmed, Nayem Naziullah, Shekh Islam, Md Nahidul Ab Rashid, Mamunur F.F. |
| author_facet | Islam, Thamina Ahmed, Firoz Ahmed, Nayem Naziullah, Shekh Islam, Md Nahidul Ab Rashid, Mamunur F.F. |
| author_sort | Islam, Thamina |
| collection | UMP |
| description | Hearing loss is a prevalent impairment that disrupts interactions with others and individuals' learning abilities. Immediate and accurate diagnosis of hearing loss using Electroencephalogram (EEG) signals, particularly Auditory Evoked Potentials (AEP), is considered the most effective approach to address this issue. The AEP signals, generated in the cerebral cortex in response to auditory stimuli, serve as the most reliable method for diagnosing deafness. This study introduces a novel approach for detecting hearing ability through the classification of EEG-AEP signals. The current experiment makes use of a publicly available dataset that contains AEP responses from 16 people who responded to auditory stimuli on either the left or right side. Sample Entropy is employed to extract the feature, capturing the complex temporal dynamics of the EEG signals. Four popular machine learning-based classifiers, namely Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), and Logistic Regression (LR), are utilized for classification purposes. The results indicate that SVM achieves the highest classification accuracy of 99.37% with subject-4 and the average accuracy of 90.74% is achieved with all subjects. This finding shows the effectiveness of Sample Entropy as a feature extraction technique for characterizing AEPs and highlights the potential of SVM as a robust classifier for the accurate identification of auditory stimuli localization. The accuracy achieved in this study indicates a promising direction for the development of reliable and non-invasive methods for hearing-related diagnoses. |
| first_indexed | 2024-04-22T01:26:05Z |
| format | Conference or Workshop Item |
| id | UMPir40384 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-04-22T01:26:05Z |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| record_format | dspace |
| spelling | UMPir403842024-04-16T04:19:45Z http://umpir.ump.edu.my/id/eprint/40384/ Classification of EEG-based auditory evoked potentials using entropy-based features and machine learning techniques Islam, Thamina Ahmed, Firoz Ahmed, Nayem Naziullah, Shekh Islam, Md Nahidul Ab Rashid, Mamunur F.F. T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Hearing loss is a prevalent impairment that disrupts interactions with others and individuals' learning abilities. Immediate and accurate diagnosis of hearing loss using Electroencephalogram (EEG) signals, particularly Auditory Evoked Potentials (AEP), is considered the most effective approach to address this issue. The AEP signals, generated in the cerebral cortex in response to auditory stimuli, serve as the most reliable method for diagnosing deafness. This study introduces a novel approach for detecting hearing ability through the classification of EEG-AEP signals. The current experiment makes use of a publicly available dataset that contains AEP responses from 16 people who responded to auditory stimuli on either the left or right side. Sample Entropy is employed to extract the feature, capturing the complex temporal dynamics of the EEG signals. Four popular machine learning-based classifiers, namely Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), and Logistic Regression (LR), are utilized for classification purposes. The results indicate that SVM achieves the highest classification accuracy of 99.37% with subject-4 and the average accuracy of 90.74% is achieved with all subjects. This finding shows the effectiveness of Sample Entropy as a feature extraction technique for characterizing AEPs and highlights the potential of SVM as a robust classifier for the accurate identification of auditory stimuli localization. The accuracy achieved in this study indicates a promising direction for the development of reliable and non-invasive methods for hearing-related diagnoses. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40384/1/Classification%20of%20EEG-based%20auditory%20evoked%20potentials.pdf pdf en http://umpir.ump.edu.my/id/eprint/40384/2/Classification%20of%20EEG-based%20auditory%20evoked%20potentials%20using%20entropy-based%20features%20and%20machine%20learning%20techniques_ABS.pdf Islam, Thamina and Ahmed, Firoz and Ahmed, Nayem and Naziullah, Shekh and Islam, Md Nahidul and Ab Rashid, Mamunur F.F. (2023) Classification of EEG-based auditory evoked potentials using entropy-based features and machine learning techniques. In: 2nd International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2023 , 21-23 September 2023 , Dhaka. pp. 124-128. (194316). ISBN 979-835035866-7 https://doi.org/10.1109/ICICT4SD59951.2023.10303426 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Islam, Thamina Ahmed, Firoz Ahmed, Nayem Naziullah, Shekh Islam, Md Nahidul Ab Rashid, Mamunur F.F. Classification of EEG-based auditory evoked potentials using entropy-based features and machine learning techniques |
| title | Classification of EEG-based auditory evoked potentials using entropy-based features and machine learning techniques |
| title_full | Classification of EEG-based auditory evoked potentials using entropy-based features and machine learning techniques |
| title_fullStr | Classification of EEG-based auditory evoked potentials using entropy-based features and machine learning techniques |
| title_full_unstemmed | Classification of EEG-based auditory evoked potentials using entropy-based features and machine learning techniques |
| title_short | Classification of EEG-based auditory evoked potentials using entropy-based features and machine learning techniques |
| title_sort | classification of eeg based auditory evoked potentials using entropy based features and machine learning techniques |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/40384/1/Classification%20of%20EEG-based%20auditory%20evoked%20potentials.pdf http://umpir.ump.edu.my/id/eprint/40384/2/Classification%20of%20EEG-based%20auditory%20evoked%20potentials%20using%20entropy-based%20features%20and%20machine%20learning%20techniques_ABS.pdf |
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