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المؤلفAbualsaud K.
المؤلفMahmuddin M.
المؤلفSaleh M.
المؤلفMohamed A.
تاريخ الإتاحة2022-04-21T08:58:33Z
تاريخ النشر2014
اسم المنشورProceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
المصدرScopus
المعرّفhttp://dx.doi.org/10.1109/AICCSA.2014.7073258
معرّف المصادر الموحدhttp://hdl.handle.net/10576/30153
الملخصIdentification 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.
اللغةen
الناشرIEEE Computer Society
الموضوعAlgorithms
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)
العنوانPerformance Comparison of classification algorithms for EEG-based remote epileptic seizure detection in Wireless Sensor Networks
النوعConference Paper
الصفحات633-639
رقم المجلد2014
dc.accessType Abstract Only


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