Title :
Tracking tremor frequency in spike trains using the extended Kalman smoother
Author :
Sunghan Kim ; McNames, J.
Author_Institution :
Dept. of Electr. & Comput. Eng., Portland State Univ., OR
Abstract :
Tremor is one of the most disabling symptoms in patients with many movement disorders including Parkinson´s disease (PD) and essential tremor (ET). Neural tremor manifests itself as a quasi-periodic fluctuation of the firing rate. We describe a frequency tracking method based on the extended Kalman smoother (EKS) to estimate the instantaneous tremor frequency (ITF) exhibited in binary spike trains detected from neural recordings. Simulation results demonstrate that the EKS frequency tracker can estimate the ITF accurately, even though the signal of interest is not sinusoidal and the noise is not Gaussian. The EKS frequency tracker can obtain a normalized mean squared error (NMSE) as low as 0.1 and performs much better than the conventional approach based on the Hilbert transform
Keywords :
Hilbert transforms; Kalman filters; bioelectric phenomena; biomechanics; diseases; mean square error methods; medical signal processing; neurophysiology; smoothing methods; Hilbert transform; Parkinson disease; binary spike trains; essential tremor; extended Kalman smoother; firing rate; instantaneous tremor frequency; movement disorder; neural recordings; neural tremor; normalized mean squared error; quasiperiodic fluctuation; spike trains; tremor frequency tracking; Biomedical computing; Biomedical signal processing; Electromyography; Fluctuations; Frequency estimation; Instruments; Kalman filters; Microelectrodes; Parkinson´s disease; Signal processing; Binary spike trains; Hilbert transform; Parkinson´s disease (PD); essential tremor (ET); extended Kalman filter (EKF); instantaneous frequency; interspike interval; microelectrode recordings (MER); renewal process; spike detection; Action Potentials; Algorithms; Computer Simulation; Diagnosis, Computer-Assisted; Electromyography; Models, Neurological; Models, Statistical; Muscle Contraction; Muscle, Skeletal; Pattern Recognition, Automated; Signal Processing, Computer-Assisted; Stochastic Processes; Systems Theory; Tremor;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2006.877809
