Epileptic Seizure Prediction Using Patient-Adaptive Transformer Networks
Summary: arXiv:2603.26821v1 Announce Type: cross
Abstract
Epileptic seizure prediction from electroencephalographic (EEG) recordings remains challenging due to strong inter-patient variability and the complex temporal structure of neural signals. This paper presents a patient-adaptive transformer framework for short-horizon seizure forecasting.
Introduction
The prediction of epileptic seizures is a critical area of research that has significant implications for improving the quality of life for patients suffering from epilepsy. Traditional methods of seizure prediction have faced numerous challenges, primarily due to the high variability of EEG signals among different patients and the intricate temporal patterns inherent in these signals.
Methodology
The proposed approach utilizes a two-stage training strategy:
- Self-supervised Pretraining: This stage focuses on learning general EEG temporal representations through autoregressive sequence modeling. By leveraging a self-supervised learning approach, the model can understand the underlying temporal dynamics of EEG data without requiring extensive labeled datasets.
- Patient-specific Fine-tuning: Following the pretraining phase, the model undergoes fine-tuning tailored to individual patients. This step is crucial for achieving binary prediction of seizure onset within a 30-second horizon, allowing for timely interventions.
Data Processing
To facilitate effective sequence learning with the transformer architecture, multichannel EEG signals are subjected to a series of preprocessing steps:
- Noise-aware Preprocessing: This step ensures that the data fed into the model is as clean and relevant as possible, reducing noise that could impede the learning process.
- Tokenization: The preprocessed EEG signals are then discretized into tokenized temporal sequences, enabling the transformer model to process the data efficiently.
Results
The effectiveness of the proposed patient-adaptive transformer framework was evaluated using subjects from the TUH EEG dataset. The experiments yielded impressive results, with validation accuracies exceeding 90% and F1 scores surpassing 0.80 across the evaluated patients. These metrics highlight the potential of combining self-supervised representation learning with patient-specific adaptation for individualized seizure prediction.
Conclusion
The findings of this study underscore the importance of personalized approaches in the field of seizure prediction. By employing a patient-adaptive transformer framework, the research demonstrates a significant advancement in the ability to predict epileptic seizures, paving the way for improved patient outcomes and more effective management of epilepsy.
Future Work
Future research will focus on expanding the dataset to include a wider variety of patients and EEG patterns, as well as refining the model to enhance its predictive capabilities. The integration of additional data sources, such as behavioral and environmental factors, may also contribute to more robust seizure prediction systems.