Purpose. The objective of this work is to compare the behavior of two electroencephalogram (EEG) based indices after drug induction and during recovery of consciousness.Methods. Data was recorded from 140 patients scheduled for general anaesthesia with a combination of propofol and remifentanil. The qCON 2000 monitor (Quantium Medical, Barcelona, Spain) was used to calculate the qCON and qNOX. The fall times and the rise times were defined at the start and at the end of the surgery. Loss of response to verbal command and loss of eye-lash reflex were assessed during the transition from awake to anesthetized, defining the state of loss of consciousness (LOC). Movement as a response to laryngeal mask (LMA) insertion was interpreted as the response to the nociceptive stimuli. The patients were classified as movers or non-movers. The values of qCON and qNOX were statistically compared. Conclusion.The indices qCON and qNOX were able to detect differences between the times of actions of hypnotic and analgesic agents. The qCON showed faster decrease during induction while the qNOX showed a faster increase during recovery.
7Excessive daytime sleepiness (EDS) is one of the main symptoms of several sleep related disorders with a great impact on the 8 patient lives. While many studies have been carried out in order to assess daytime sleepiness, the automatic EDS detection still remains 9 an open problem. In this work, a novel approach to this issue based on non-linear dynamical analysis of EEG signal was proposed. However these algorithms present methodologies and results of mixed quality and weakness as small sample size, lack of 38 cross validation analysis (or other acknowledgement/accommodation of individual variance), task 39 dependence/specificity, algorithm complexity and large number of channels required. Furthermore, since many features 40 of EEG signals cannot be generated by linear models, it is generally argued that non-linear measures are likely to give 41 more information than the ones obtained with conventional linear approaches. 42In the last few years, nonlinear techniques have been used to comprehend complex dynamics of the underlying 43 neurophysiological processes [17,18] and to detect nonlinear interactions [19]. The fundamental assumption of nonlinear 44 techniques is that the EEG signal is generated by nonlinear deterministic processes with nonlinear coupling interactions 45 between neuronal populations. Nonlinearity in the brain likely occurs, even at the cellular level [20]
BackgroundWe recently demonstrated that quality of spirometry in primary care could markedly improve with remote offline support from specialized professionals. It is hypothesized that implementation of automatic online assessment of quality of spirometry using information and communication technologies may significantly enhance the potential for extensive deployment of a high quality spirometry program in integrated care settings.ObjectiveThe objective of the study was to elaborate and validate a Clinical Decision Support System (CDSS) for automatic online quality assessment of spirometry.MethodsThe CDSS was done through a three step process including: (1) identification of optimal sampling frequency; (2) iterations to build-up an initial version using the 24 standard spirometry curves recommended by the American Thoracic Society; and (3) iterations to refine the CDSS using 270 curves from 90 patients. In each of these steps the results were checked against one expert. Finally, 778 spirometry curves from 291 patients were analyzed for validation purposes.ResultsThe CDSS generated appropriate online classification and certification in 685/778 (88.1%) of spirometry testing, with 96% sensitivity and 95% specificity.ConclusionsConsequently, only 93/778 (11.9%) of spirometry testing required offline remote classification by an expert, indicating a potential positive role of the CDSS in the deployment of a high quality spirometry program in an integrated care setting.
Abstract:The level of sedation in patients undergoing medical procedures is decided to assure unconsciousness and prevent pain. 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 refined multiscale entropy (RMSE) and auto mutual information function (AMIF) applied to EEG signals recorded in 378 patients scheduled to undergo ultrasonographic endoscopy under sedation-analgesia. Two observed categorical responses after the application of painful stimulation were analyzed: the evaluation of the Ramsay Sedation Scale (RSS) after nail bed compression and the presence of gag reflex (GAG) during endoscopy tube insertion. In addition, bispectrum (BIS), heart rate (HR), predicted concentrations of propofol (CeProp) and remifentanil (CeRemi) were annotated with a resolution of 1 s. Results showed that functions based on RMSE, AMIF, HR and CeRemi permitted predicting different stimulation responses during sedation better than BIS.
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