title: PureEEG: automatic EEG artifact removal for epilepsy monitoring.
abstract: OBJECTIVE
  A novel method for removal of artifacts from long-term EEGs was developed and evaluated. The method targets most types of artifacts and works without user interaction.
  
  
  METHODS
  The method is based on a neurophysiological model and utilizes an iterative Bayesian estimation scheme. The performance was evaluated by two independent reviewers. From 48 consecutive epilepsy patients, 102 twenty-second seizure onset EEGs were used to evaluate artifacts before and after artifact removal and regarding the erroneous attenuation of true EEG patterns.
  
  
  RESULTS
  The two reviewers found "major improvements" in 59% and 49% of the EEG epochs respectively, and "minor improvements" in 38% and 47% of the epochs, respectively. The answer "similar or worse" was chosen only in 0% and 4%, respectively. Neither of the reviewers found "major attenuations", i.e., a significant attenuation of significant EEG patterns. Most EEG epochs were found to be either "mostly preserved" or "all preserved". A "minor attenuation" was found only in 0% and 17%, respectively.
  
  
  CONCLUSIONS
  The proposed artifact removal algorithm effectively removes artifacts from EEGs and improves the readability of EEGs impaired by artifacts. Only in rare cases did the algorithm slightly attenuate EEG patterns, but the clear visibility of significant patterns was preserved in all cases of this study. Current artifact removal methods work either semi-automatically or with insufficient reliability for clinical use, whereas the "PureEEG" method works fully automatically and leaves true EEG patterns unchanged with a high reliability.
authors.affiliation: AIT Austrian Institute of Technology GmbH, Donau-City-Strasse 1, 1220 Vienna, Austria. Electronic address: manfred.hartmann@ait.ac.at.
authors.forename: M M
authors.initials: MM
authors.lastname: Hartmann
authors.affiliation: Department of Neurology, Inselspital Bern, Bern University Hospital, 3010 Bern, Switzerland.
authors.forename: K
authors.initials: K
authors.lastname: Schindler
authors.affiliation: University Medical Center Utrecht, Huispostnummer G03.131, Postbus 85500, 3508 GA Utrecht, Netherlands.
authors.forename: T A
authors.initials: TA
authors.lastname: Gebbink
authors.affiliation: AIT Austrian Institute of Technology GmbH, Donau-City-Strasse 1, 1220 Vienna, Austria.
authors.forename: G
authors.initials: G
authors.lastname: Gritsch
authors.affiliation: AIT Austrian Institute of Technology GmbH, Donau-City-Strasse 1, 1220 Vienna, Austria.
authors.forename: T
authors.initials: T
authors.lastname: Kluge
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001499:Bayes Theorem; D004569:Electroencephalography / Q000295:instrumentation / Q000379:methods*; D001330:Electronic Data Processing; D004827:Epilepsy / Q000503:physiopathology*; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D008991:Monitoring, Physiologic / Q000295:instrumentation / Q000379:methods*; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted
keywords: Analyse automatique; Artefacts; Artifact removal; Artifacts; Automatic; Crise; EEG; Epilepsy; PureEEG; Seizure; Suppression d’artefacts; Épilepsie