title: Automatic seizure detection using correlation integral with nonlinear adaptive denoising and Kalman filter.
abstract: The ability of correlation integral for automatic seizure detection using scalp EEG data has been re-examined in this paper. To facilitate the detection performance and overcome the shortcoming of correlation integral, nonlinear adaptive denoising and Kalman filter have been adopted for pre-processing and post-processing. The three-stage algorithm has achieved 84.6% sensitivity and 0.087/h false detection rate, which are comparable to many machine learning based methods, but at much lower computational cost. Since this algorithm is tested with long-term scalp EEG, it has the potential to achieve higher performance with intracranial EEG. The clinical value of this algorithm includes providing a pre-judgement to assist the doctor's diagnosis procedure and acting as a reliable warning system in a wearable device for epilepsy patients.
authors.lastname: Hongda Wang
authors.lastname: Chiu-Sing Choy
mesh_terms: D000465:Algorithms; D004569:Electroencephalography; D004827:Epilepsy / Q000175:diagnosis*; D006801:Humans; D017711:Nonlinear Dynamics; D012640:Seizures / Q000175:diagnosis*; D012680:Sensitivity and Specificity