title: A novel dynamic update framework for epileptic seizure prediction.
abstract: Epileptic seizure prediction is a difficult problem in clinical applications, and it has the potential to significantly improve the patients' daily lives whose seizures cannot be controlled by either drugs or surgery. However, most current studies of epileptic seizure prediction focus on high sensitivity and low false-positive rate only and lack the flexibility for a variety of epileptic seizures and patients' physical conditions. Therefore, a novel dynamic update framework for epileptic seizure prediction is proposed in this paper. In this framework, two basic sample pools are constructed and updated dynamically. Furthermore, the prediction model can be updated to be the most appropriate one for the prediction of seizures' arrival. Mahalanobis distance is introduced in this part to solve the problem of side information, measuring the distance between two data sets. In addition, a multichannel feature extraction method based on Hilbert-Huang transform and extreme learning machine is utilized to extract the features of a patient's preseizure state against the normal state. At last, a dynamic update epileptic seizure prediction system is built up. Simulations on Freiburg database show that the proposed system has a better performance than the one without update. The research of this paper is significantly helpful for clinical applications, especially for the exploitation of online portable devices.
authors: Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China.
authors: Min
authors: 0000-0002-2964-4884
authors: M
authors: Han
authors: Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China.
authors: Sunan
authors: 0000-0003-2491-3552
authors: S
authors: Ge
authors: Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China.
authors: Minghui
authors: M
authors: Wang
authors: Department of Neurology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China.
authors: Xiaojun
authors: X
authors: Hong
authors: Department of Neurology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China.
authors: Jie
authors: J
authors: Han
mesh_terms: D001185:Artificial Intelligence; D004569:Electroencephalography; D004827:Epilepsy / Q000175:diagnosis*; D006801:Humans; D008959:Models, Neurological; D012815:Signal Processing, Computer-Assisted