title: Epileptic seizure prediction using phase synchronization based on bivariate empirical mode decomposition.
abstract: OBJECTIVE
  Epilepsy is a common neurological disorder with unpredictability. An effective algorithm for seizure prediction is important for the patients with refractory epilepsy.
  
  
  METHODS
  We proposed a seizure prediction method based on the phase synchronization information of neuronal electrical activities. Firstly, the instantaneous phase of the intracranial electroencephalograph (EEG) recordings was detected by the combination of bivariate empirical mode decomposition (BEMD) and Hilbert transformation. Then, the phase information was used to calculate the mean phase coherence (MPC) as a measure of phase coupling strength between different channels of EEG recordings. In the end, the preictal changes of MPC time courses were used to raise the seizure alarms. We compared the proposed method with other existing methods to further investigate its effectiveness.
  
  
  RESULTS
  Both the increase and the decrease of phase synchronization were found prior to seizure onset. Our results indicated that the proposed method had the best performance among three predictors.
  
  
  CONCLUSIONS
  The proposed algorithm can effectively extract the phase synchrony changes prior to the seizure onset and contribute to the application of the seizure prediction.
  
  
  CONCLUSIONS
  Phase synchronization analysis based on the BEMD method may be a useful algorithm for clinical application in epileptic prediction.
authors.affiliation: The Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, National Engineering Research Center of Health Care and Medical Devices, Xi'an Jiaotong University Branch, Xi'an 710049, PR China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Zheng
authors.affiliation: The Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, National Engineering Research Center of Health Care and Medical Devices, Xi'an Jiaotong University Branch, Xi'an 710049, PR China. Electronic address: ggwang@mail.xjtu.edu.cn.
authors.forename: Gang
authors.initials: G
authors.lastname: Wang
authors.affiliation: First Affiliated Hospital, Xi'an Jiaotong University, Xi'an 710061, PR China.
authors.forename: Kuo
authors.initials: K
authors.lastname: Li
authors.affiliation: First Affiliated Hospital, Xi'an Jiaotong University, Xi'an 710061, PR China.
authors.forename: Gang
authors.initials: G
authors.lastname: Bao
authors.affiliation: The Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, National Engineering Research Center of Health Care and Medical Devices, Xi'an Jiaotong University Branch, Xi'an 710049, PR China. Electronic address: juewang1@126.com.
authors.forename: Jue
authors.initials: J
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D004569:Electroencephalography / Q000379:methods*; D058407:Electroencephalography Phase Synchronization; D004827:Epilepsy / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity
keywords: Bivariate empirical mode decomposition; Electroencephalogram; Phase synchronization; Seizure prediction