NDLI: A new stochastic estimator for tremor frequency tracking

Content Provider	IEEE Xplore Digital Library
Author	Kucukelbir, A. Kushki, A. Plataniotis, K.N.
Copyright Year	2009
Description	Author affiliation: The Edward S. Rogers Sr. Dept. of Electrical & Computer Engineering, University of Toronto, Canada (Kucukelbir, A.; Kushki, A.; Plataniotis, K.N.)
Abstract	An important parameter in analysis of physiological tremor is the diagnosis and study of neurological disorders. The instantaneous tremor frequency (ITF) is an important parameter in tremor analysis. This paper proposes a novel stochastic filter, the multiple extended Kalman filter (M-EKF), for tracking of ITF from neural microelectrode recordings. The M-EKF mitigates degradations in filter performance resulting from a mismatch between assumed initial conditions and those of a particular realization of a stochastic system. Specifically, the M-EKF is comprised of a bank of extended Kalman filters (EKF), each initialized with different conditions, selected according to the unscented transform. The final estimate is a weighted average of the individual estimates provided by each EKF where the weights reflect how closely the assumed EKF initial conditions match those of the true system. The M-EKF is applied to a synthetic tremor model to display its superior performance to that of the EKF and the unscented Kalman filter.
Starting Page	421
Ending Page	424
File Size	217372
Page Count	4
File Format	PDF
ISBN	9781424423538
ISSN	15206149
DOI	10.1109/ICASSP.2009.4959610
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-19
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Frequency estimation Stochastic processes State estimation Stochastic systems Stochastic resonance Neural microtechnology Degradation Kalman filters Filtering Time measurement extended Kalman filtering Tremor frequency state-space model nonlinear estimation unscented transform
Content Type	Text
Resource Type	Article

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