Sample Entropy on Multidistance Signal Level Difference for Epileptic EEG Classification

Epilepsy is a disorder of the brain’s nerves as a result of excessive brain cell activity. It is generally characterized by the recurrent unprovoked seizures. This neurological abnormality can be detected and evaluated using Electroencephalogram (EEG) signal. Many algorithms have been applied to ach...

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Bibliographic Details
Published in:TheScientificWorld 2018, Vol.2018 (2018), p.1-6
Main Authors: Rizal, Achmad, Hadiyoso, Sugondo
Format: Article
Language:English
Subjects:
Algorithms
Alzheimer's disease
Anesthesia
Brain
Brain - physiopathology
Brain research
Classification
Convulsions & seizures
Datasets
EEG
Electroencephalography
Electroencephalography - classification
Electroencephalography - methods
Entropy
Epilepsy
Epilepsy - classification
Epilepsy - physiopathology
Humans
International conferences
Methods
Nerves
Neural networks
Physiology
Seizing
Seizures
Signal processing
Support Vector Machine - classification
Support vector machines
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Summary:Epilepsy is a disorder of the brain’s nerves as a result of excessive brain cell activity. It is generally characterized by the recurrent unprovoked seizures. This neurological abnormality can be detected and evaluated using Electroencephalogram (EEG) signal. Many algorithms have been applied to achieve high performance for the EEG classification of epileptic. However, the complexity and randomness of EEG signals become a challenge to researchers in applying the appropriate algorithms. In this research, sample entropy on Multidistance Signal Level Difference (MSLD) was applied to obtain the characteristic of EEG signals, especially towards the epilepsy patients. The test was performed on three classes of EEG data: EEG signals of epilepsy patient in ictal (seizure), interictal conditions (occurring between seizures) and normal EEG signals from healthy subjects with a closed eye condition. In this study, classification and verification were done using the Support Vector Machine (SVM) method. Through the 5-fold cross-validation, experimental results showed the highest accuracy of 97.7%.
ISSN:2356-6140
1537-744X
1537-744X
DOI:10.1155/2018/8463256