Segmentation of EMG time series using a variational Bayesian approach for the robust estimation of cortical silent periods
نویسندگان
چکیده
Abstract. A variational Bayesian formulation for a manifold-constrained Hidden Markov Model is used in this paper to segment a set of multivariate time series of electromyographic recordings corresponding to stroke patients and control subjects. An index of variability associated to this model is defined and applied to the robust detection of the silent period interval of the signal. The accuracy in the estimation of the duration of this interval is paramount to assess the rehabilitation of stroke patients.
منابع مشابه
A variational Bayesian approach for the robust analysis of the cortical silent period from EMG recordings of brain stroke patients
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 progno...
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