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Addressing data limitations in seizure prediction through transfer learning.
Lopes, Fábio; Pinto, Mauro F; Dourado, António; Schulze-Bonhage, Andreas; Dümpelmann, Matthias; Teixeira, César.
Afiliação
  • Lopes F; Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal. fadcl@dei.uc.pt.
  • Pinto MF; Department Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany. fadcl@dei.uc.pt.
  • Dourado A; Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal.
  • Schulze-Bonhage A; Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal.
  • Dümpelmann M; Department Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Teixeira C; Department Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Sci Rep ; 14(1): 14169, 2024 06 19.
Article em En | MEDLINE | ID: mdl-38898066
ABSTRACT
According to the literature, seizure prediction models should be developed following a patient-specific approach. However, seizures are usually very rare events, meaning the number of events that may be used to optimise seizure prediction approaches is limited. To overcome such constraint, we analysed the possibility of using data from patients from an external database to improve patient-specific seizure prediction models. We present seizure prediction models trained using a transfer learning procedure. We trained a deep convolutional autoencoder using electroencephalogram data from 41 patients collected from the EPILEPSIAE database. Then, a bidirectional long short-term memory and a classifier layers were added on the top of the encoder part and were optimised for 24 patients from the Universitätsklinikum Freiburg individually. The encoder was used as a feature extraction module. Therefore, its weights were not changed during the patient-specific training. Experimental results showed that seizure prediction models optimised using pretrained weights present about four times fewer false alarms while maintaining the same ability to predict seizures and achieved more 13% validated patients. Therefore, results evidenced that the optimisation using transfer learning was more stable and faster, saving computational resources. In summary, adopting transfer learning for seizure prediction models represents a significant advancement. It addresses the data limitation seen in the seizure prediction field and offers more efficient and stable training, conserving computational resources. Additionally, despite the compact size, transfer learning allows to easily share data knowledge due to fewer ethical restrictions and lower storage requirements. The convolutional autoencoder developed in this study will be shared with the scientific community, promoting further research.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Convulsões / Eletroencefalografia Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Convulsões / Eletroencefalografia Idioma: En Ano de publicação: 2024 Tipo de documento: Article