Research Area:  Machine Learning
In the past decade, the rapid development of machine learning has dramatically improved the performance of epileptic detection with Electroencephalography (EEG). However, only a small amount of labeled epileptic data is available for training because labeling requires numerous neurologists. This paper proposes a one-step semi-supervised epilepsy detection system to reduce the labeling cost by fully utilizing the unlabeled data. The proposed neural network training strategy enables a more robust and accurate decision boundary by forcing the consistency of the double predictions on the same unlabeled data. The results show that the Area Under Receiver Operating Characteristic (AUROC) curves of our proposed model are 10.3% and 4.9% higher than the supervised methods on CHB-MIT and Kaggle datasets, respectively.
Author(s) Name:  Zheng Zhang; Xin Li; Fengji Geng; Kejie Huang
Conferrence name:  43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Publisher name:  IEEE
Paper Link:   https://ieeexplore.ieee.org/abstract/document/9630363