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author:

Liu, Rongchang (Liu, Rongchang.) [1] | Tang, Yunbo (Tang, Yunbo.) [2] | Huang, Weirong (Huang, Weirong.) [3] | Lin, Qifeng (Lin, Qifeng.) [4] | Yu, Yuanlong (Yu, Yuanlong.) [5]

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Abstract:

Epileptic seizure prediction has been widely investigated to provide timely warning for patients with epilepsy in clinical practice. However, current methods have limited prediction capabilities as it remains a grand challenge to properly handle the intensive intra-individual and inter-individual variabilities along with the information fusion of EEG features from various domains. To address the issues, this study proposes a multi-domain EEG feature fusion network with supervised contrastive learning (namely MDFFNet-SCL) for epileptic seizure prediction. MDFFNet-SCL first employs five types of data transformations to generate the positive samples of anchor EEG samples. Multi-domain EEG features are then computed to reveal the multi-level information related to epileptic seizure, which is fed into EEG encoders with multi-scale convolution network to independently extract the deep EEG representations. In consideration of the inter-domain and intra-domain correlations, deep canonical correlation analysis (DCCA) is exploited to merge the deep EEG representations in multiple domains and form the shared representations. Finally, MDFFNet-SCL applies cross-entropy classification loss combined with CCA and SCL losses for model training, where CCA and SCL losses are utilized to increase the correlation between multi-domain features and alleviate the intra-individual and inter-individual variabilities. Experimental results on two public EEG datasets (CHB-MIT, Kaggle) indicate that (1) multi-domain feature fusion and supervised contrastive learning schemes have demonstrated superiority in capturing discriminative EEG features between the preictal and interictal states, (2) MDFFNet-SCL significantly outperforms the state-of-the-art counterparts with accuracy and AUC up to 98.70%,99.89% for CHB-MIT EEG dataset and 96.95%,99.27% for Kaggle EEG dataset. The solution can be extended to design a more general & robust framework for cross-subject or cross-dataset seizure prediction. © 2025 Elsevier Ltd

Keyword:

Classification (of information) Contrastive Learning Electroencephalography Forecasting Information fusion Learning algorithms Learning systems Metadata Neurodegenerative diseases Neurophysiology Supervised learning

Community:

  • [ 1 ] [Liu, Rongchang]College of Computer and Data Science, Fuzhou University, Fuzhou, China
  • [ 2 ] [Tang, Yunbo]College of Computer and Data Science, Fuzhou University, Fuzhou, China
  • [ 3 ] [Huang, Weirong]College of Computer and Data Science, Fuzhou University, Fuzhou, China
  • [ 4 ] [Lin, Qifeng]College of Computer and Data Science, Fuzhou University, Fuzhou, China
  • [ 5 ] [Yu, Yuanlong]College of Computer and Data Science, Fuzhou University, Fuzhou, China

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Source :

Biomedical Signal Processing and Control

ISSN: 1746-8094

Year: 2026

Volume: 112

4 . 9 0 0

JCR@2023

Cited Count:

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SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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