Performance Comparison of classification algorithms for EEG-based remote epileptic seizure detection in Wireless Sensor Networks
Author | Abualsaud K. |
Author | Mahmuddin M. |
Author | Saleh M. |
Author | Mohamed A. |
Available date | 2022-04-21T08:58:33Z |
Publication Date | 2014 |
Publication Name | Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA |
Resource | Scopus |
Identifier | http://dx.doi.org/10.1109/AICCSA.2014.7073258 |
Abstract | 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 IEEE. |
Language | en |
Publisher | IEEE Computer Society |
Subject | Algorithms Classifiers Data mining Discrete cosine transforms Electroencephalography Electrophysiology Feature extraction Neurodegenerative diseases Neurophysiology Wireless sensor networks Classification accuracy Classification algorithm Epileptic seizure detection Epileptic seizures Performance comparison Statistical features Wavelet coefficients Wide-ranging applications Classification (of information) |
Type | Conference Paper |
Pagination | 633-639 |
Volume Number | 2014 |
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