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-awar...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2015/945689 |
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| author | Khalid Abualsaud Massudi Mahmuddin Mohammad Saleh Amr Mohamed |
| author_facet | Khalid Abualsaud Massudi Mahmuddin Mohammad Saleh Amr Mohamed |
| author_sort | Khalid Abualsaud |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-8eae8beab959467ba654f088a3f70ff5 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-8eae8beab959467ba654f088a3f70ff52025-02-03T05:59:15ZengWileyThe Scientific World Journal2356-61401537-744X2015-01-01201510.1155/2015/945689945689Ensemble Classifier for Epileptic Seizure Detection for Imperfect EEG DataKhalid Abualsaud0Massudi Mahmuddin1Mohammad Saleh2Amr Mohamed3Department of Computer Science & Engineering, College of Engineering, Qatar University, P.O. Box 2713, Doha, QatarComputer Science Department, Graduate School of Computing, University Utara Malaysia (UUM), 06010 Sintok, Kedah, MalaysiaDepartment of Computer Science & Engineering, College of Engineering, Qatar University, P.O. Box 2713, Doha, QatarDepartment of Computer Science & Engineering, College of Engineering, Qatar University, P.O. Box 2713, Doha, QatarBrain 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.http://dx.doi.org/10.1155/2015/945689 |
| spellingShingle | Khalid Abualsaud Massudi Mahmuddin Mohammad Saleh Amr Mohamed Ensemble Classifier for Epileptic Seizure Detection for Imperfect EEG Data The Scientific World Journal |
| 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 |
| url | http://dx.doi.org/10.1155/2015/945689 |
| work_keys_str_mv | AT khalidabualsaud ensembleclassifierforepilepticseizuredetectionforimperfecteegdata AT massudimahmuddin ensembleclassifierforepilepticseizuredetectionforimperfecteegdata AT mohammadsaleh ensembleclassifierforepilepticseizuredetectionforimperfecteegdata AT amrmohamed ensembleclassifierforepilepticseizuredetectionforimperfecteegdata |