Título:
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Advanced Statistical Machine Learning Methods for the Analysis of Neurophysiologic Data with Medical Application
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Autor/a:
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Amengual Roig, Julià Lluís
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Otros autores:
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Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Vellido Alcacena, Alfredo |
Abstract:
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Transcranial magnetic stimulation procedures use a magnetic field to carry a short-lasting electrical
current pulse into the brain, where it stimulates neurons, particularly in superficial regions
of the cerebral cortex. It is a powerfull tool to calculate several parameters related to the intracortical
excitability and inhibition of the motor cortex. The cortical silent period (CSP), evoked
by magnetic stimulation, corresponds to the suppression of muscle activity for a short period after
a muscle response to a magnetic stimulation. The duration of the CSP is paramount to assess
intracortical inhibition, and it is known to be correlated with the prognosis of stroke patients’
motor ability. Current mechanisms to estimate the duration of the CSP are mostly based on the
analysis of raw electromyographical (EMG) signal and they are very sensitive to the presence of
noise.
This master thesis is devoted to the analysis of the EMG signal of stroke patients under rehabilitation.
The use of advanced statistical machine learning techniques that behave robustly in
the presence of noise for this analysis allows us to accurately estimate signal parameters such
as the CSP. The research reported in this thesis provides us with a first evidence about their
applicability in other areas of neuroscience. |
Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Bioinformàtica -Cerebral embolism and thrombosis -Brain stimulation -Electromiography -Stroke -Variational Bayesian Generative -Topograp of Variability -Silent Periohic Mapping -Embòlia i trombosi cerebral -Cervell -- Estimulació |
Derechos:
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Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Tipo de documento:
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Trabajo fin de máster |
Editor:
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Universitat Politècnica de Catalunya
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