Olier, I, Amengual, J and Vellido, A (2011) A variational Bayesian approach for the robust analysis of the cortical silent period from EMG recordings of brain stroke patients. Neurocomputing, 74 (9). 1301 -1314.

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Abstract

Transcranial magnetic stimulation (TMS) is a powerful tool for the calculation of parameters related to the intracortical excitability and inhibition of the motor cortex. The cortical silent period (CSP) is one such parameter that corresponds to the suppression of muscle activity for a short period after a muscle response to TMS. The duration of the CSP is known to be correlated with the prognosis of brain stroke patients' motor ability. Current methods for the estimation of the CSP duration are very sensitive to the presence of noise. A variational Bayesian formulation of a manifold-constrained hidden Markov model is applied in this paper to the segmentation of a set of multivariate time series (MTS) of electromyographic recordings corresponding to stroke patients and control subjects. A novel index of variability associated to this model is defined and applied to the detection of the silent period interval of the signal and to the estimation of its duration. This model and its associated index are shown to behave robustly in the presence of noise and provide more reliable estimations than the current standard in clinical practice.

Item Type: Article
Uncontrolled Keywords: multivariate time series, manifold learning, variational Bayesian generative topographic mapping, index of variability, electromyography, brain stroke, cortical silent period
Subjects: R Medicine > R Medicine (General)
Divisions: Faculty of Medicine and Health Sciences > Institute for Science and Technology in Medicine
Depositing User: Symplectic
Date Deposited: 04 Nov 2015 12:44
Last Modified: 04 Jun 2019 11:54
URI: http://eprints.keele.ac.uk/id/eprint/1098

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