TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification - Ruimin Peng; Changming Zhao; Jun Jiang; Guangtao Kuang; Yuqi Cui; Yifan Xu; Hao Du; Jianbo Shao; Dongrui Wu (IEEE Transactions on Neural Systems and Rehabilitation Engineering)
Electroencephalogram (EEG) based seizure subtype classification is very important in clinical diagnostics. However, manual seizure subtype classification is expensive and time-consuming, whereas automatic classification usually needs a large number of labeled samples for model training. This paper proposes an EEGNet-based slim deep neural network, which relieves the labeled data requirement in EEG-based seizure subtype classification. A temporal information enhancement module with sinusoidal encoding is used to augment the first convolution layer of EEGNet. A training strategy for automatic hyper-parameter selection is also proposed. Experiments on the public TUSZ dataset and our own CHSZ dataset with infants and children demonstrated that our proposed TIE-EEGNet outperformed several traditional and deep learning models in cross-subject seizure subtype classification. Additionally, it also achieved the best performance in a challenging transfer learning scenario. Both our code and the CHSZ dataset are publicized.