Related papers: Bridging the Gap Between Patient-specific and Patient-independent Seizure Prediction via Knowledge Distillation

Bridging the Gap Between Patient-specific and Patient-independent Seizure Prediction via Knowledge Distillation

URL: http://arxiv.org/abs/2202.12598v1
Date: Fri, 25 Feb 2022 10:30:29 GMT
Title: Bridging the Gap Between Patient-specific and Patient-independent Seizure Prediction via Knowledge Distillation
Authors: Di Wu, Jie Yang, and Mohamad Sawan
Abstract summary: Existing approaches typically train models in a patient-specific fashion due to the highly personalized characteristics of epileptic signals. A patient-specific model can then be obtained with the help of distilled knowledge and additional personalized data. Five state-of-the-art seizure prediction methods are trained on the CHB-MIT sEEG database with our proposed scheme.
Score: 7.2666838978096875
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Objective. Deep neural networks (DNN) have shown unprecedented success in various brain-machine interface (BCI) applications such as epileptic seizure prediction. However, existing approaches typically train models in a patient-specific fashion due to the highly personalized characteristics of epileptic signals. Therefore, only a limited number of labeled recordings from each subject can be used for training. As a consequence, current DNN based methods demonstrate poor generalization ability to some extent due to the insufficiency of training data. On the other hand, patient-independent models attempt to utilize more patient data to train a universal model for all patients by pooling patient data together. Despite different techniques applied, results show that patient-independent models perform worse than patient-specific models due to high individual variation across patients. A substantial gap thus exists between patient-specific and patient-independent models. In this paper, we propose a novel training scheme based on knowledge distillation which makes use of a large amount of data from multiple subjects. It first distills informative features from signals of all available subjects with a pre-trained general model. A patient-specific model can then be obtained with the help of distilled knowledge and additional personalized data. Significance. The proposed training scheme significantly improves the performance of patient-specific seizure predictors and bridges the gap between patient-specific and patient-independent predictors. Five state-of-the-art seizure prediction methods are trained on the CHB-MIT sEEG database with our proposed scheme. The resulting accuracy, sensitivity, and false prediction rate show that our proposed training scheme consistently improves the prediction performance of state-of-the-art methods by a large margin.