Privacy-preserving multi-source semi-supervised domain adaptation for seizure prediction

Domain adaptation (DA) has been frequently used to solve the inter-patient variability problem in EEG-based seizure prediction. However, existing DA methods require access to the existing patients' data when adapting the model, which leads to privacy concerns. Besides, most of them treat the whole existing patients' data as one single source and attempt to minimize the discrepancy with the target patient. This manner ignores the inter-patient variability among source patients, making the adaptation more difficult. Considering theses issues simultaneously, we present a novel multi-source-free semi-supervised domain adaptive seizure prediction model (MSF-SSDA-SPM). MSF-SSDA-SPM considers each source patient as one single source and generates a pretrained model from each source. Without requiring access to the source data, MSF-SSDA-SPM performs adaptation just using these pretrained source models and limited labeled target data. Specifically, we freeze the classifiers of all the source models and optimize the source feature extractors in a joint manner. Then we design a knowledge distillation strategy to integrate the knowledge of these well-adapted source models into one single target model. On the CHB-MIT dataset, MSF-SSDA-SPM attains a sensitivity of 88.6%, a FPR of 0.182/h and an AUC of 0.856; on the Kaggle dataset, it achieves 78.6%, 0.178/h and 0.784, respectively. Experimental results demonstrate that MSF-SSDA-SPM achieves both high privacy-protection and promising prediction performance.