Ensemble classifier for epileptic seizure detection for imperfect EEG data
Brain status information is captured by physiological electroencephalogram (EEG) signals, which are extensively used to study different brain activities.This study investigates the use of a new ensemble classifier to detect an epileptic seizure from compressed and noisy EEG signals.This noise-aware...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Hindawi Publishing Corporation
2015
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| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/15060/1/Ensemble.pdf |
| _version_ | 1803626903230218240 |
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| author | Abualsaud, Khalid Mahmuddin, Massudi Saleh, Mohammad Mohamed, Amr |
| author_facet | Abualsaud, Khalid Mahmuddin, Massudi Saleh, Mohammad Mohamed, Amr |
| author_sort | Abualsaud, Khalid |
| collection | UUM |
| description | Brain status information is captured by physiological electroencephalogram (EEG) signals, which are extensively used to study different brain activities.This study investigates the use of a new ensemble classifier to detect an epileptic seizure from compressed and noisy EEG signals.This noise-aware signal combination (NSC) ensemble classifier combines four classification models based on their individual performance.The main objective of the proposed classifier is to enhance the classification accuracy in the presence of noisy and incomplete information while preserving a reasonable amount of complexity.The experimental results show the effectiveness of the NSC technique, which yields higher accuracies of 90% for noiseless data compared with 85%, 85.9%, and 89.5% in other experiments.The accuracy for the proposed method is 80% when SNR = 1 dB, 84% when SNR = 5 dB, and 88% when SNR = 10 dB, while the compression ratio (CR) is 85.35% for all of the datasets mentioned. |
| first_indexed | 2024-07-04T05:57:33Z |
| format | Article |
| id | uum-15060 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T05:57:33Z |
| publishDate | 2015 |
| publisher | Hindawi Publishing Corporation |
| record_format | dspace |
| spelling | uum-150602016-04-26T08:34:10Z https://repo.uum.edu.my/id/eprint/15060/ Ensemble classifier for epileptic seizure detection for imperfect EEG data Abualsaud, Khalid Mahmuddin, Massudi Saleh, Mohammad Mohamed, Amr QA76 Computer software Brain status information is captured by physiological electroencephalogram (EEG) signals, which are extensively used to study different brain activities.This study investigates the use of a new ensemble classifier to detect an epileptic seizure from compressed and noisy EEG signals.This noise-aware signal combination (NSC) ensemble classifier combines four classification models based on their individual performance.The main objective of the proposed classifier is to enhance the classification accuracy in the presence of noisy and incomplete information while preserving a reasonable amount of complexity.The experimental results show the effectiveness of the NSC technique, which yields higher accuracies of 90% for noiseless data compared with 85%, 85.9%, and 89.5% in other experiments.The accuracy for the proposed method is 80% when SNR = 1 dB, 84% when SNR = 5 dB, and 88% when SNR = 10 dB, while the compression ratio (CR) is 85.35% for all of the datasets mentioned. Hindawi Publishing Corporation 2015 Article PeerReviewed application/pdf en cc_by https://repo.uum.edu.my/id/eprint/15060/1/Ensemble.pdf Abualsaud, Khalid and Mahmuddin, Massudi and Saleh, Mohammad and Mohamed, Amr (2015) Ensemble classifier for epileptic seizure detection for imperfect EEG data. The Scientific World Journal, 2015. pp. 1-15. ISSN 2356-6140 http://doi.org/10.1155/2015/945689 doi:10.1155/2015/945689 doi:10.1155/2015/945689 |
| spellingShingle | QA76 Computer software Abualsaud, Khalid Mahmuddin, Massudi Saleh, Mohammad Mohamed, Amr Ensemble classifier for epileptic seizure detection for imperfect EEG data |
| title | Ensemble classifier for epileptic seizure detection for imperfect EEG data |
| title_full | Ensemble classifier for epileptic seizure detection for imperfect EEG data |
| title_fullStr | Ensemble classifier for epileptic seizure detection for imperfect EEG data |
| title_full_unstemmed | Ensemble classifier for epileptic seizure detection for imperfect EEG data |
| title_short | Ensemble classifier for epileptic seizure detection for imperfect EEG data |
| title_sort | ensemble classifier for epileptic seizure detection for imperfect eeg data |
| topic | QA76 Computer software |
| url | https://repo.uum.edu.my/id/eprint/15060/1/Ensemble.pdf |
| work_keys_str_mv | AT abualsaudkhalid ensembleclassifierforepilepticseizuredetectionforimperfecteegdata AT mahmuddinmassudi ensembleclassifierforepilepticseizuredetectionforimperfecteegdata AT salehmohammad ensembleclassifierforepilepticseizuredetectionforimperfecteegdata AT mohamedamr ensembleclassifierforepilepticseizuredetectionforimperfecteegdata |