Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks
Identification of epileptic seizure remotely by analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems.Classification is the most important technique for wide-ranging applications to categorize the items according to its features with respect t...
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my.uum.repo.150622016-04-27T04:18:20Z http://repo.uum.edu.my/15062/ Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks Abualsaud, Khalid Mahmuddin, Massudi Saleh, Mohammad Mohamed, Amr QA76 Computer software Identification of epileptic seizure remotely by analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems.Classification is the most important technique for wide-ranging applications to categorize the items according to its features with respect to predefined set of classes.In this paper, we conduct a performance evaluation based on the noiseless and noisy EEG-based epileptic seizure data using various classification algorithms including BayesNet, DecisionTable, IBK, J48/C4.5, and VFI.The reconstructed and noisy EEG data are decomposed with discrete cosine transform into several sub-bands.In addition, some of statistical features are extracted from the wavelet coefficients to represent the whole EEG data inputs into the classifiers.Benchmark on widely used dataset is utilized for automatic epileptic seizure detection including both normal and epileptic EEG datasets.The classification accuracy results confirm that the selected classifiers have greater potentiality to identify the noisy epileptic disorders. 2014-11-10 Conference or Workshop Item PeerReviewed application/pdf en http://repo.uum.edu.my/15062/1/0091aiccsa2014.pdf Abualsaud, Khalid and Mahmuddin, Massudi and Saleh, Mohammad and Mohamed, Amr (2014) Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks. In: The 11th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA'2014), November 10-13, 2014, InterContinental, West Bay, Doha, Qatar. http://cse.qu.edu.qa/aiccsa2014/ |
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QA76 Computer software Abualsaud, Khalid Mahmuddin, Massudi Saleh, Mohammad Mohamed, Amr Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks |
description |
Identification of epileptic seizure remotely by
analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems.Classification is the most important technique for wide-ranging applications to
categorize the items according to its features with respect to predefined set of classes.In this paper, we conduct a performance evaluation based on the noiseless and noisy EEG-based epileptic
seizure data using various classification algorithms including BayesNet, DecisionTable, IBK, J48/C4.5, and VFI.The reconstructed and noisy EEG data are decomposed with discrete cosine transform into several sub-bands.In addition, some of
statistical features are extracted from the wavelet coefficients to represent the whole EEG data inputs into the classifiers.Benchmark on widely used dataset is utilized for automatic
epileptic seizure detection including both normal and epileptic EEG datasets.The classification accuracy results confirm that the selected classifiers have greater potentiality to identify the noisy epileptic disorders. |
format |
Conference or Workshop Item |
author |
Abualsaud, Khalid Mahmuddin, Massudi Saleh, Mohammad Mohamed, Amr |
author_facet |
Abualsaud, Khalid Mahmuddin, Massudi Saleh, Mohammad Mohamed, Amr |
author_sort |
Abualsaud, Khalid |
title |
Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks |
title_short |
Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks |
title_full |
Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks |
title_fullStr |
Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks |
title_full_unstemmed |
Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks |
title_sort |
performance comparison of classification algorithms for eeg-based remote epileptic seizure detection in wireless sensor networks |
publishDate |
2014 |
url |
http://repo.uum.edu.my/15062/1/0091aiccsa2014.pdf http://repo.uum.edu.my/15062/ http://cse.qu.edu.qa/aiccsa2014/ |
_version_ |
1644281620012728320 |
score |
13.154905 |