title: Automatic detector of high frequency oscillations for human recordings with macroelectrodes.
abstract: High Frequency Oscillations (HFOs) in the EEG are a promising biomarker of epileptogenic tissue. Given that the visual marking of HFOs is highly time-consuming and subjective, automatic detectors are necessary. In this study, we present a novel automatic detector that detects HFOs by incorporating information of previously detected baselines. The detector was trained on 72 channels and tested on 278, achieving a mean sensitivity of 96.8% with a mean false positive rate of 4.86%. This low rate is reasonable since only visually marked baseline segments were considered as the true negatives. This detector could be useful for the systematic study of HFOs and for their eventual clinical application.
authors.affiliation: Department of Biomedical Engineering at McGill University and the Montreal Neurological Institute, Montreal, QC, H3A 2B4, Canada. rina.zelmann@mail.mcgill.ca
authors.forename: R
authors.initials: R
authors.lastname: Zelmann
authors.forename: F
authors.initials: F
authors.lastname: Mari
authors.forename: J
authors.initials: J
authors.lastname: Jacobs
authors.forename: M
authors.initials: M
authors.lastname: Zijlmans
authors.forename: R
authors.initials: R
authors.lastname: Chander
authors.forename: J
authors.initials: J
authors.lastname: Gotman
mesh_terms: D001331:Automation; D001931:Brain Mapping / Q000379:methods; D004567:Electrodes, Implanted; D004827:Epilepsy / Q000601:surgery*; D004867:Equipment Design; D005189:False Positive Reactions; D006801:Humans; D015233:Models, Statistical; D009991:Oscillometry / Q000379:methods*; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted