Epileptic Disorder Detection of Seizures Using EEG Signals

Sensors (Basel). 2022 Aug 31;22(17):6592. doi: 10.3390/s22176592.


Epilepsy is a nervous system disorder. Encephalography (EEG) is a generally utilized clinical approach for recording electrical activity in the brain. Although there are a number of datasets available, most of them are imbalanced due to the presence of fewer epileptic EEG signals compared with non-epileptic EEG signals. This research aims to study the possibility of integrating local EEG signals from an epilepsy center in King Abdulaziz University hospital into the CHB-MIT dataset by applying a new compatibility framework for data integration. The framework comprises multiple functions, which include dominant channel selection followed by the implementation of a novel algorithm for reading XLtek EEG data. The resulting integrated datasets, which contain selective channels, are tested and evaluated using a deep-learning model of 1D-CNN, Bi-LSTM, and attention. The results achieved up to 96.87% accuracy, 96.98% precision, and 96.85% sensitivity, outperforming the other latest systems that have a larger number of EEG channels.

Keywords: CHB-MIT dataset; XLtek EEG; deep learning; epilepsy; seizure detection.

MeSH terms

  • Algorithms
  • Brain
  • Electroencephalography* / methods
  • Epilepsy* / diagnosis
  • Humans
  • Seizures / diagnosis
  • Signal Processing, Computer-Assisted