ABSTRACT
This letter is devoted to the suppression of spurious signals (artifacts) in records of neural activity during deep brain stimulation. An approach based on nonlinear adaptive model with self-oscillations is proposed. We developed an algorithm of adaptive filtering based on this approach. The proposed algorithm was tested using recordings collected from patients during the stimulation. This was then compared to existing methods and showed the best performance.
Subject(s)
Biological Clocks/physiology , Brain/cytology , Deep Brain Stimulation/methods , Neurons/physiology , Nonlinear Dynamics , Signal Processing, Computer-Assisted , Action Potentials/physiology , Algorithms , Biophysical Phenomena , Electric Stimulation/methods , Humans , Neural Networks, ComputerABSTRACT
The study of EEG recordings during the interval prior to an epileptic seizure onset--the preictal period--is likely to detect changes in the ongoing brain activity consistent with seizure anticipation. A novel index of spectral instability (ISpI) based on multiple abrupt changes of EEG spectral features is presented here. Based on the analysis of control records, robust M-estimates are used to calculate the threshold and avoid false warnings. The results obtained with a small data set (three patients, ten preictal records per patient) have shown that the ISpI index provided a warning flag that anticipated the seizure onset by 13.1 (SD = 4.0) min on average.
