A variational Bayesian approach for the robust analysis of the cortical silent period from EMG recordings of brain stroke patients

Authors:
Iván Olier;Julií Amengual;Alfredo Vellido
Affiliations:
School of Psychological Sciences, The University of Manchester, Zochonis Building, M13 9PL Manchester, United Kingdom;Neurodynamics Laboratory, Department of Psychiatry and Clinical Psychobiology, Universitat de Barcelona, 08035 Barcelona, Spain;Dept. de Llenguatges i Sistemes Informítics - Universitat Politècnica de Catalunya, Edifici Omega, Campus Nord, 08034 Barcelona, Spain
Venue:
Neurocomputing
Year:
2011

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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.