dc.contributor |
Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
dc.contributor |
Vallverdú Ferrer, Montserrat |
dc.contributor.author |
Fort Grèbol, Pol |
dc.date |
2014-09 |
dc.identifier.uri |
http://hdl.handle.net/2099.1/24226 |
dc.language.iso |
eng |
dc.publisher |
Universitat Politècnica de Catalunya |
dc.rights |
Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.rights |
http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject |
Àrees temàtiques de la UPC::Ciències de la salut::Medicina |
dc.subject |
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal |
dc.subject |
Electroencephalography |
dc.subject |
Signal processing -- Computer simulation |
dc.subject |
Algorithms |
dc.subject |
Electroencefalografia |
dc.subject |
Tractament del senyal -- Simulació per ordinador |
dc.subject |
Algorismes |
dc.title |
Symbolic Dynamics applied to Electroencephalographic signals to Predict Response to Noxious Stimulation during Sedation-Analgesia |
dc.type |
info:eu-repo/semantics/bachelorThesis |
dc.description.abstract |
The level of sedation in patients undergoing medical procedures evolves continuously since the effect
of the anesthetic and analgesic agents is counteracted by noxious stimuli. The monitors of depth of
anesthesia, based on the analysis of the electroencephalogram (EEG), have been progressively
introduced into the daily practice to provide additional information about the state of the patient.
However, the quantification of analgesia still remains an open problem.
In this project, a methodology based on non-linear techniques signal processing algorithms was
developed and applied to the electroencephalogram (EEG) for predicting responses to noxious
stimulation during Sedation-Analgesia. Two types of stimuli were performed by the anesthesiologist
during the surgery sessions, RSS (Ramsay Sedation Scale) and GAG (gag reflex). These sedation
scales are considered gold standard. In this work, the scope of the project includes: EEG
preprocessing, processing and analysis of the mentioned signals.
The methodology included an EEG signal preprocessing, a time-domain and frequency-domain
analysis, the development and application of non-linear techniques, a statistical analysis and finally the
validation of the results. Symbolic dynamics methodology, already applied to other kind of signals,
was used as a non-linear technique. The aim was to extract a set of patterns from the EEG obtained
through two proposed non-linear algorithms.
The symbolic dynamics consists of the transformation of the time signal in a series of symbols by an
algorithm. From these new series, words of three symbols were constructed with one symbol delay and
their occurrence probability was evaluated in the signals variables. Base on this, the Shannon and
Rényi entropies were applied to estimate the complexity of the distribution of the variables. Moreover,
thresholds on probabilities were used to construct new variables. The analysis was applied to the EEG
filtered according to the characteristic frequency bands (EEG rhythms). The parameters involved in the
algorithms were statistically adjusted in order to better characterize the nociceptive response. Variables
obtained from linear and non-linear methodologies were submitted to a statistical analysis using a nonparametric
test and a linear discriminant analyses to assess the quality of the classification. The
leaving-one-out method was used as validation criteria. New defined variables were able to describe the different states with p-value < 6.88E-6, Spe > 60%, Sen > 60% and Pk > 0,6. This signal processing methodology technically contributes to the
prediction of anesthesia depth level during Sedation-Analgesia. |