Why the study?
Previous EEG seizure detection methods rarely accounted for positional correlations between channels and faced challenges with data imbalance between seizure and nonseizure durations.
A novel graph attention network with focal loss demonstrates high accuracy, sensitivity, and specificity for automatic seizure detection on EEG signals.
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May enhance automated EEG seizure detection via channel correlations; leaves open prospective clinical validation before practice change.
Automatic seizure detection from electroencephalogram (EEG) plays a vital role in accelerating epilepsy diagnosis. Previous researches on seizure detection mainly focused on extracting time-domain and frequency-domain features from single electrodes, while paying little attention to the positional correlations between different EEG channels of the same subject. Moreover, data imbalance is common in seizure detection scenarios where the duration of nonseizure periods is much longer than the duration of seizures. To cope with the two challenges, a novel seizure detection method based on graph attention network (GAT) is presented. The approach acts on graph-structured data and takes the raw EEG data as input. The positional relationship between different EEG signals is exploited by GAT. The loss function of the GAT model is redefined using the focal loss to tackle data imbalance problem. Experiments are conducted on the CHB-MIT dataset. The accuracy, sensitivity and specificity of the proposed method are 98.89[Formula: see text], 97.10[Formula: see text] and 99.63[Formula: see text], respectively.
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Zhao et al. (2021) studied this question.
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