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• W4313431595 abstract "Epilepsy is a neurological disorder related to frequent seizures. Automatic seizure prediction is crucial for the prevention and treatment of epilepsy. In this paper, we propose a novel model for seizure prediction that incorporates a convolutional neural network (CNN) with multi-head attention mechanism. In this model, the shallow CNN automatically captures the EEG features, and the multi-headed attention focuses on discriminating the effective information among these features for identifying pre-ictal EEG segments. Compared with current CNN models for seizure prediction, the embedded multi-headed attention empowers the shallow CNN to be more flexible, and enables improvement of the training efficiency. Hence, this compact model is more resistant to being trapped in overfitting. The proposed method was evaluated over the scalp EEG data from the two publicly available epileptic EEG databases, and achieved outperforming values of event-level sensitivity, false prediction rate (FPR), and epoch-level F1. Furthermore, our method achieved the stable length of seizure prediction time that was between 14 and 15 min. The experimental comparisons showed that our method outperformed other prediction methods in terms of prediction and generalization performance." @default.
• W4313431595 created "2023-01-06" @default.
• W4313431595 creator A5018140935 @default.
• W4313431595 creator A5019180907 @default.
• W4313431595 creator A5046614213 @default.
• W4313431595 creator A5056899169 @default.
• W4313431595 creator A5080191032 @default.
• W4313431595 date "2023-02-23" @default.
• W4313431595 modified "2023-10-01" @default.
• W4313431595 title "Compact Convolutional Neural Network with Multi-Headed Attention Mechanism for Seizure Prediction" @default.
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• W4313431595 doi "https://doi.org/10.1142/s0129065723500144" @default.
• W4313431595 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36811492" @default.
• W4313431595 hasPublicationYear "2023" @default.
• W4313431595 type Work @default.
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