  Background: Epilepsy is a neurological illness affecting the brain that makes
people more likely to experience frequent, spontaneous seizures. There has to
be an accurate automated method for measuring seizure frequency and severity in
order to assess the efficacy of pharmacological therapy for epilepsy. The drug
quantities are often derived from patient reports which may cause significant
issues owing to inadequate or inaccurate descriptions of seizures and their
frequencies. Methods and materials: This study proposes a novel deep learning
architecture based lightweight convolution transformer (LCT). The transformer
is able to learn spatial and temporal correlated information simultaneously
from the multi-channel electroencephalogram (EEG) signal to detect seizures at
smaller segment lengths. In the proposed model, the lack of translation
equivariance and localization of ViT is reduced using convolution tokenization,
and rich information from the transformer encoder is extracted by sequence
pooling instead of the learnable class token. Results: Extensive experimental
results demonstrate that the proposed model of cross-patient learning can
effectively detect seizures from the raw EEG signals. The accuracy and F1-score
of seizure detection in the cross-patient case on the CHB-MIT dataset are shown
to be 96.31% and 96.32%, respectively, at 0.5 sec segment length. In addition,
the performance metrics show that the inclusion of inductive biases and
attention-based pooling in the model enhances the performance and reduces the
number of transformer encoder layers, which significantly reduces the
computational complexity. In this research work, we provided a novel approach
to enhance efficiency and simplify the architecture for multi-channel automated
seizure detection.
