Prior knowledge guided logistic regression model with group lasso penalty for modeling epilepsy disease prediction

"Small sample size, high dimension" data bring tremendous challenges to epilepsy Electroencephalography (EEG) data analysis and seizure onset prediction. Commonly, sparsity technique is introduced to tackle the problem. In this paper, we construct a indicator matrix acting as prior knowledge to assist logistic regression model with group lasso penalty to implement seizure prediction. The proposed method selects the feature at the group level, and it achieves the seizure prediction based on the important feature groups, recognizes the unknown clusters properly and performs well for both synthetic data following Bernoulli distribution and dataset CHB-MIT.