TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification

• Main Authors: Ruimin Peng (Author), Changming Zhao (Author), Jun Jiang (Author), Guangtao Kuang (Author), Yuqi Cui (Author), Yifan Xu (Author), Hao Du (Author), Jianbo Shao (Author), Dongrui Wu (Author)
• Format: Book
• Published: IEEE, 2022-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

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.

Item Description

1558-0210
10.1109/TNSRE.2022.3204540