title: Predicting epileptic seizures from scalp EEG based on attractor state analysis.
abstract: OBJECTIVE
  Epilepsy is the second most common disease of the brain. Epilepsy makes it difficult for patients to live a normal life because it is difficult to predict when seizures will occur. In this regard, if seizures could be predicted a reasonable period of time before their occurrence, epilepsy patients could take precautions against them and improve their safety and quality of life. In this paper, we investigate a novel seizure precursor based on attractor state analysis for seizure prediction.
  
  
  METHODS
  We analyze the transition process from normal to seizure attractor state and investigate a precursor phenomenon seen before reaching the seizure attractor state. From the result of an analysis, we define a quantified spectral measure in scalp EEG for seizure prediction. From scalp EEG recordings, the Fourier coefficients of six EEG frequency bands are extracted, and the defined spectral measure is computed based on the coefficients for each half-overlapped 20-second-long window. The computed spectral measure is applied to seizure prediction using a low-complexity methodology.
  
  
  RESULTS
  Within scalp EEG, we identified an early-warning indicator before an epileptic seizure occurs. Getting closer to the bifurcation point that triggers the transition from normal to seizure state, the power spectral density of low frequency bands of the perturbation of an attractor in the EEG, showed a relative increase. A low-complexity seizure prediction algorithm using this feature was evaluated, using ∼583h of scalp EEG in which 143 seizures in 16 patients were recorded. With the test dataset, the proposed method showed high sensitivity (86.67%) with a false prediction rate of 0.367h-1and average prediction time of 45.3min.
  
  
  CONCLUSIONS
  A novel seizure prediction method using scalp EEG, based on attractor state analysis, shows potential for application with real epilepsy patients. This is the first study in which the seizure-precursor phenomenon of an epileptic seizure is investigated based on attractor-based analysis of the macroscopic dynamics of the brain. With the scalp EEG, we first propose use of a spectral feature identified for seizure prediction, in which the dynamics of an attractor are excluded, and only the perturbation dynamics from the attractor are considered.
authors.affiliation: Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Daejeon, Republic of Korea.
authors.forename: Hyunho
authors.initials: H
authors.lastname: Chu
authors.affiliation: Department of Neurosurgery, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Brain and Cognitive Science, Seoul National University College of Natural Science, Seoul, Republic of Korea.
authors.forename: Chun Kee
authors.initials: CK
authors.lastname: Chung
authors.affiliation: Interdisciplinary Program in Neuroscience, Seoul National University College of Natural Science, Seoul, Republic of Korea.
authors.forename: Woorim
authors.initials: W
authors.lastname: Jeong
authors.affiliation: Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Daejeon, Republic of Korea. Electronic address: ckh@kaist.ac.kr.
authors.forename: Kwang-Hyun
authors.initials: KH
authors.lastname: Cho
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging; D001931:Brain Mapping; D003936:Diagnosis, Computer-Assisted; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000000981:diagnostic imaging*; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D008297:Male; D009474:Neurons / Q000473:pathology; D011237:Predictive Value of Tests; D012535:Scalp; D012680:Sensitivity and Specificity
keywords: Attractor state; Early-warning indicator; Electroencephalography (EEG); Epileptic seizure; Spectral feature; Systems biology; prediction