Both Cross-Patient and Patient-Specific Seizure Detection Based on Self-Organizing Fuzzy Logic

Author:

Zhou Jiazheng1,Liu Li1,Leng Yan1,Yang Yuying1,Gao Bin1,Jiang Zonghong2,Nie Weiwei3,Yuan Qi1

Affiliation:

1. Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250358, P. R. China

2. College of Resources and Environment Engineering, Guizhou University, Guiyang 550025, P. R. China

3. The First Affiliated Hospital of Shandong, First Medical University, Jinan 250014, P. R. China

Abstract

Automatic epilepsy detection is of great significance for the diagnosis and treatment of patients. Most detection methods are based on patient-specific models and have achieved good results. However, in practice, new patients do not have their own previous EEG data and therefore cannot be initially diagnosed. If the EEG data of other patients can be used to achieve cross-patient detection, and cross-patient and patient-specific experiments can be combined at the same time, this method will be more widely used. In this work, an EEG classification model based on a self-organizing fuzzy logic (SOF) classifier is proposed for both cross-patient and patient-specific seizure detection. After preprocessing, the features of the original EEG signal are extracted and sent to the SOF classifier. This classification model is free from predefined parameters or a prior assumption regarding the EEG data generation model and only stores the key meta-parameters in memory. Therefore, it is very suitable for large-scale EEG signals in cross-patient detection. Selecting different granularity and classification distance in two different experiments after post-processing will achieve the best results. Experiments were conducted using a long-term continuous scalp EEG database and the [Formula: see text]-mean of cross-patient and patient-specific detection reached 83.35% and 92.04%, respectively. A comparison with other methods shows that there is greater performance and generalizability with this method.

Funder

National Natural Science Foundation of China

Shandong Provincial Natural Science Foundation

NChina Postdoctoral Science Foundation

Publisher

World Scientific Pub Co Pte Ltd

Subject

Computer Networks and Communications,General Medicine

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