title: An automatic patient-specific seizure onset detection method using intracranial electroencephalography.
abstract: OBJECTIVE
  This study presents a multichannel patient-specific seizure detection method based on the empirical mode decomposition (EMD) and support vector machine (SVM) classifier.
  
  
  METHODS
  The EMD is used to extract features from intracranial electroencephalography (EEG). A machine-learning algorithm is used as a classifier to discriminate between seizure and nonseizure intracranial EEG epochs. A postprocessing algorithm is proposed to reject artifacts and increase the robustness of the method. The proposed method was evaluated using 463 hours of intracranial EEG recordings from 17 patients with a total of 51 seizures in the Freiburg EEG database.
  
  
  RESULTS
  The proposed method had better performance than most of the existing seizure detection systems, including an average sensitivity of 92%, false detection rate (FDR) of 0.17/hour, and time delay (TD) of 12 sec. Moreover, the FDR could be further reduced by a TD extension.
  
  
  CONCLUSIONS
  Given its high sensitivity and low FDR, the proposed patient-specific seizure detection method can greatly assist clinical staff with automatically marking seizures in long-term EEG or detecting seizure onset online with high performance. Early and accurate seizure detection using this method may serve as a practical tool for planning epilepsy interventions.
authors.affiliation: Department of Neurosurgery, The Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
authors.forename: Yu-xin
authors.initials: YX
authors.lastname: Zheng
authors.forename: Jun-ming
authors.initials: JM
authors.lastname: Zhu
authors.forename: Yu
authors.initials: Y
authors.lastname: Qi
authors.forename: Xiao-xiang
authors.initials: XX
authors.lastname: Zheng
authors.forename: Jian-min
authors.initials: JM
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D004569:Electroencephalography; D001330:Electronic Data Processing; D005260:Female; D006801:Humans; D008297:Male; D012640:Seizures / Q000175:diagnosis* / Q000503:physiopathology*; D060388:Support Vector Machine; D013997:Time Factors
keywords: Empirical mode decomposition; epilepsy; intracranial EEG; seizure detection; support vector machine