Auto-Mutual Information Function for Predicting Pain Responses in EEG Signals during Sedation
The level of sedation in patients undergoing medical procedures evolves continuously, such as the effect of the anesthetic and analgesic agents is counteracted by pain 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. The purpose of this work was to analyze the capability of prediction of nociceptive responses based on the auto-mutual information function (AMIF). AMIF measures were calculated on EEG signal in order to predict the presence or absence of the nociceptive responses to endoscopy tube insertion during sedation in endoscopy procedure. Values of prediction probability of Pk above 0.80 and percentages of sensitivity and specificity above 70% and 70% respectively were achieved combining AMIF with power spectral density and concentrations of remifentanil.
KeywordsBiomedical signal processing electroencephalogram information theory complexity
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