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An interpretable and generalizable deep learning model for iEEG-based seizure prediction using prototype learning and

Yikai Gao1, Aiping Liu2, Heng Cui2

  • 1Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China; Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230001, China.

Computers in Biology and Medicine
|October 18, 2024
PubMed
Summary

This study introduces an interpretable deep learning model for epilepsy seizure prediction. The new method improves generalization across patients, offering better clinical application potential.

Keywords:
Deep learningGeneralizabilityInterpretabilityIntracranial electroencephalographySeizure predictionSignal processing

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Epileptic seizure prediction is vital for patient quality of life.
  • Deep learning models show promise but lack interpretability and generalization.
  • Interpatient variability hinders clinical application of current models.

Purpose of the Study:

  • Develop an interpretable and generalizable seizure prediction model for clinical use.
  • Address the 'black-box' nature and generalization limitations of deep learning in epilepsy.
  • Enable patient-level interpretability beyond individual sample analysis.

Main Methods:

  • Extended self-interpretable prototype learning networks into a domain adaptation framework.
  • Implemented patient-level interpretability by tracing prototype origins.
  • Introduced contrastive semantic alignment loss for improved prototype robustness and generalization.

Main Results:

  • Achieved high sensitivity (79.0%) and AUC (0.804) on the Freiburg iEEG dataset.
  • Demonstrated a low false prediction rate (0.183).
  • Outperformed existing cross-patient seizure prediction methods with self-interpretable evidence.

Conclusions:

  • The proposed model offers a significant advancement in interpretable and generalizable seizure prediction.
  • The framework facilitates the clinical application of deep learning for epilepsy diagnosis.
  • Patient-level interpretability enhances trust and utility in clinical settings.