Transformer-based Models for Seizure Prediction from EEG Time-series Data (S29.004)

Fnu Vaibhav; Vipul Kaliraman; Aditya Duhan; Simran Cheema; Ankit Lnu; Pardeep Kumar

doi:10.1212/wnl.0000000000217883

June 9, 2026Neurology

Transformer-based Models for Seizure Prediction from EEG Time-series Data (S29.004)

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Key Points

This work aims to validate the utility of transformer-based models for predicting seizures from EEG data in clinical settings.
Developed patient-specific transformer models for preictal seizure detection from EEG time-series data.
Conducted validation studies to assess performance in clinical environments.
Achieved high accuracy in preictal seizure detection using transformers compared to traditional methods.
Demonstrated potential for improved patient care in epilepsy management.

Implication

Validates transformer models for preictal seizure prediction in clinical settings, indicating improved epilepsy care.

Abstract

Transformers enable precise preictal detection by modeling EEG dynamics. This work validates their utility in clinical settings, focusing on patient-specific models for better epilepsy care and practical deployment.

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Cite This Study

Vaibhav et al. (2026) studied this question.

synapsesocial.com/papers/6a2cf3b4faef96ed7f05627d https://doi.org/10.1212/wnl.0000000000217883

View Full Paper

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Transformer-based Models for Seizure Prediction from EEG Time-series Data (S29.004)

Key Points

Implication

Abstract

Discussion

Cite This Study

Transformer-based Models for Seizure Prediction from EEG Time-series Data (S29.004)

Key Points

Implication

Abstract

Discussion

Cite This Study

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