This paper presents an approach for filtering signals of neuronal activity during Deep Brain Stimulation (DBS) using nonlinear oscillatory models.
Adaptive Model for Filtering of Stimulation Artifacts in Multichannel Records of Neuronal Activity Nowicki D.V.* u , Benabid A.-L1 Aksenova T.I. U 1
Unit 318, INSERM, CHUA. Michallon, BP 217, 38043 Grenoble, Cedex 09, France 2 Institute of Applied System Analysis, Ukrainian Academy of Sciences, Prospekt Peremogy, 37, Kiev 03056, Ukraine 3 Institute ofMathematical Machines and Systems 42 Glushkov ave., 03187 Kiev Ukraine nowicki(q)/nip- ups-tlse.fr Alim-Louis. Benabid(a>,uff-Srenoble. fr tatyana.aksyonova&uif-grenoble.fr
Abstract. The present paper is devoted to suppression of spurious signals (artifacts) in records of neural activity during deep brain stimulation. An approach based on a nonlinear adaptive model with self-oscillations is proposed. Keywords: Adaptive modelling, adaptive filtering, phase space, neuronal activity, deep brain stimulation, artifacts. PACS: 02.30.Hq, 07.05.Tp, 87.19.La
INTRODUCTION This paper presents an approach for filtering signals of neuronal activity during Deep Brain Stimulation (DBS) using nonlinear oscillatory models. High-frequency (100-300 Hz) DBS is a family of surgical procedure for treating a variety of disabling neurological symptoms. In spite of its clinical efficiency the mechanism of action of DBS is still a matter of debate [1]. The major difficulty to study the mechanism is that the appropriate signal of neuronal activity, namely the extracellular microelectrode recording of action potentials (spikes), cannot be observed directly during the stimulation session due to stimulation artifacts present in the records (Fig. 1). The artifacts are induced by the periodically repeated electrical impulses delivered to the target zone in the brain. The artifacts have a common waveform but are not identical due to irregularities of stimulus production. The Artifact-to-Signal Ratio (ASR), which is the ratio of the mean of amplitudes of artifacts to the averaged amplitude of spikes of neuronal activity varies between 5 to 20 (Fig. 2).
D. V. Nowicki is a corresponding author
CP936, Numerical Analysis and Applied Mathematics, International Conference edited by T. E. Simos, co-edited by G. Psihoyios and Ch. Tsitouras © 2007 American Institute of Physics 978-0-7354-0447-2/07/$23.00
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