AUTHOR=Wang Baozeng , Yang Xingyi , Li Siwei , Wang Wenbo , Ouyang Yichen , Zhou Jin , Wang Changyong 

TITLE=Automatic epileptic seizure detection based on EEG using a moth-flame optimization of one-dimensional convolutional neural networks

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1291608

DOI=10.3389/fnins.2023.1291608

ISSN=1662-453X

ABSTRACT=Frequent epileptic seizures can cause irreversible damage to the brain of patients. A potential therapeutic approach is to detect epileptic seizures early and provide artificial intervention to the patient. Currently, extracting electroencephalogram (EEG) features for detecting epileptic seizures often requires tedious methods or repeated adjustment of neural network hyperparameters, which can be time-consuming and demanding for researchers. This study proposes an automatic detection model for EEG based on moth-flame optimization (MFO) optimized one-dimensional convolutional neural networks (1D-CNN). Firstly, according to the characteristics and need for early epileptic seizure detection, a data augmentation method for dividing EEG into small samples is proposed. Secondly, the hyperparameters are tuned based on MFO and trained for EEG. Finally, the softmax classifier is used to output EEG classification from a small-sample and single channel. The proposed model is evaluated with the Bonn EEG dataset, which verifies the feasibility of EEG classification problems that involve up to five classes, including healthy, preictal, and ictal EEG from various brain regions and individuals. Compared with existing advanced optimization algorithms such as particle swarm optimization, genetic algorithm, and grey wolf optimizer, the superiority of the proposed model is further verified. The proposed model can be implemented into an automatic epileptic seizure detection system to detect seizures in clinical applications.