Department of Informatics Engineering, Center for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal.
Department Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Sci Rep. 2024 Jun 19;14(1):14169. doi: 10.1038/s41598-024-64802-1.
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.
根据文献，应采用针对患者个体的方法来开发癫痫发作预测模型。然而，癫痫发作通常是非常罕见的事件，这意味着可用于优化癫痫发作预测方法的事件数量是有限的。为了克服这种限制，我们分析了使用来自外部数据库的患者数据来改进针对患者个体的癫痫发作预测模型的可能性。我们提出了使用迁移学习过程训练的癫痫发作预测模型。我们使用来自 EPILEPSIAE 数据库的 41 名患者的脑电图数据训练深度卷积自动编码器。然后，在编码器的顶部添加了双向长短期记忆和分类器层，并针对弗赖堡大学医院的 24 名患者进行了优化。编码器用作特征提取模块。因此，在针对患者个体的训练过程中，其权重不会发生变化。实验结果表明，使用预训练权重优化的癫痫发作预测模型的假警报数量减少了约四倍，同时保持了预测癫痫发作的相同能力，并实现了更多的 13%验证患者。因此，结果表明，使用迁移学习进行优化更稳定、更快，节省了计算资源。总之，采用迁移学习进行癫痫发作预测模型代表了一个重大进展。它解决了癫痫发作预测领域中的数据限制问题，并提供了更高效和稳定的训练，同时节省了计算资源。此外，尽管紧凑的尺寸，迁移学习由于较少的伦理限制和较低的存储要求，使得更容易共享数据知识。本研究中开发的卷积自动编码器将与科学界共享，促进进一步的研究。
