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

Journal: International journal of neural systems
Published Date:

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.

Authors

  • Jiazheng Zhou
    Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250358, P. R. China.
  • Li Liu
    Metanotitia Inc., Shenzhen, China.
  • Yan Leng
    Shandong Province Key Laboratory of Medical, Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Jinan 250358, P. R. China.
  • Yuying Yang
    School of Chemistry and Chemical Engineering, Shandong University of Technology, Zibo 255049, PR China.
  • Bin Gao
    Institute of Microelectronics, Tsinghua University, Beijing, 10084, China; Center for Brain-Inspired Computing Research, Tsinghua University, Beijing, 10084, China. Electronic address: gaob1@tsinghua.edu.cn.
  • Zonghong Jiang
    College of Resources and Environment Engineering, Guizhou University, Guiyang 550025, P. R. China.
  • Weiwei Nie
    Shandong Provincial Qianfoshan Hospital, Shandong First Medical University, Jinan 250014, P. R. China.
  • Qi Yuan