ArticleTheScientificWorldJournal2018
Sample Entropy on Multidistance Signal Level Difference for Epileptic EEG Classification.
Article in TheScientificWorldJournal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 41 citations in OpenAlex.
- Automatic detection of Alzheimer's disease from EEG signals using an improved AFS-GA hybrid algorithm.Cognitive neurodynamics · 2024Article
- Automatic Recognition of High-Density Epileptic EEG Using Support Vector Machine and Gradient-Boosting Decision Tree.Brain sciences · 2022Article
- An Efficient Signal Processing Algorithm for Detecting Abnormalities in EEG Signal Using CNN.Contrast media & molecular imaging · 2022Article
- Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
2 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
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%.
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