Ictal-Interictal Epileptic State Classification with Traditional and Deep Learning Architectures
Author
Erdem TUNCER
Abstract
Epileptic seizures are caused by disturbances in the
electrical activity of the brain. Failure to correctly classify epileptic forms
may result in inappropriate treatment.
Activities occurring prior to ictal activity may be causal and require further
investigation. Therefore, it is important in the diagnosis of epilepsy to distinguish between ictal and interictal
EEG using Electroencephalography (EEG) signs. In this study, ictal (absence
seizure) and interictal EEG recordings were
scored using 4 bipolar (C3-P3, T5-O1, FP2-F8, C4-T4) channel EEGs from Temple
University Hospital (TUH) EEG Seizure Corpus (TUSZ) data. The data were divided into 3-second epochs and
various features were obtained from the data. The data in each epoch were
filtered using the Discrete Wavelet Transform
(DWT) Daubechies-2 wavelet and were 0-32 Hz. range has been studied. Feature
selection was made with Correlation based Feature Selection (CFS). The performances of traditional and
deep learning classifier algorithms (Support vector machine (SVM), Long
Short-term Memory (LSTM)) were compared
and the results were discussed. The highest success rate was 96.96% in 84.86
seconds with the LSTM classifier model and 96.36% in 0.05 seconds with the SVM classifier algorithm
Keywords
Classification, Deep Learning, EEG, Epilepsy, Ictal, Interictal, Long short-term memory, Support Vector Machine, TUSZ.
DOI : https://doi.org/10.55248/gengpi.2022.3.9.53
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References
- Classification, Deep Learning, EEG, Epilepsy, Ictal, Interictal, Long short-term memory, Support Vector Machine, TUSZ.
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