Matemática e Estatística | Artigos em revistas internacionais / Papers in international journals
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Browsing Matemática e Estatística | Artigos em revistas internacionais / Papers in international journals by Author "Amorim, Pedro"
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- Feedforward adaptive control of the Bispectral Index of the EEG using the intravenous anaesthetic drug propofolPublication . Nunes, Catarina S.; Mendonça, Teresa; Lemos, João M.; Amorim, PedroThe problem of controlling the level of unconsciousness measured by the Bispectral Index (BIS) of the EEG of patients under anaesthesia is considered. The manipulated variable is the infusion rate of the hypnotic drug propofol. In order to tackle the high uncertainties present in the system, the predictive adaptive controller MUSMAR is used. The performance of the controller is illustrated by means of simulations with 50 patient individual adjusted models (nonlinear models), which incorporate the effect of the drugs interaction on BIS. This work presents a feasibility study of the control of the BIS exploring the above ideas. These results show that such a control structure can be adequate to control the BIS signal during total intravenous anaesthesia. The controller was able to adequately achieve and maintain the BIS signal with different patient dynamics, reference values and noise. A major challenge for the automation of anaesthesia consists of replicating the experience of the anaesthetist. Clearly, this calls for the use of feedforward from measurable signals correlated with disturbances (e.g. electromyography—EMG, analgesic drug). The results also show that the analgesic and EMG have different influences on the performance and therefore carry different but relevant information.
- Implementation of neural networks to frontal electroencephalography for the identification of the transition responsiveness/unresponsiveness during induction of general anesthesiaPublication . Ferreira, Ana Isabel Leitão; Vide, Sérgio; Nunes, Catarina S.; Neto, Joaquim; Amorim, Pedro; Mendes, JoaquimObjective: General anesthesia is a reversible drug-induced state of altered arousal characterized by loss of responsiveness (LOR) due to brainstem inactivation. Precise identification of the LOR during the induction of general anesthesia is extremely important to provide personalized information on anesthetic requirements and could help maintain an adequate level of anesthesia throughout surgery, ensuring safe and effective care and balancing the avoidance of intraoperative awareness and overdose. So, main objective of this paper was to investigate whether a Convolutional Neural Network (CNN) applied to bilateral frontal electroencephalography (EEG) dataset recorded from patients during opioid-propofol anesthetic procedures identified the exact moment of LOR. Material and methods: A clinical protocol was designed to allow for the characterization of different clinical endpoints throughout the transition to unresponsiveness. Fifty (50) patients were enrolled in the study and data from all was included in the final dataset analysis. While under a constant estimated effect-site concentration of 2.5 ng/mL of remifentanil, an 1% propofol infusion was started at 3.3 mL//h until LOR. The level of responsiveness was assessed by an anesthesiologist every six seconds using a modified version of the Richmond Agitation-Sedation Scale (aRASS). The frontal EEG was acquired using a bilateral bispectral (BIS VISTA (TM) v2.0, Medtronic, Ireland) sensor. EEG data was then split into 5-second epochs, and for each epoch, the anesthesiologist's classification was used to label it as responsiveness (no-LOR) or unresponsiveness (LOR). All 5-second epochs were then used as inputs for the CNN model to classify the untrained segment as no-LOR or LOR. Results: The CNN model was able to identify the transition from no-LOR to LOR successfully, achieving 97.90 +/- 0.07% accuracy on the cross-validation set. Conclusion: The obtained results showed that the proposed CNN model was quite efficient in the responsiveness/unresponsiveness classification. We consider our approach constitutes an additional technique to the current methods used in the daily clinical setting where LOR is identified by the loss of response to verbal commands or mechanical stimulus. We therefore hypothesized that automated EEG analysis could be a useful tool to detect the moment of LOR, especially using machine learning approaches.
- Neuro-fuzzy techniques to model pharmacodynamic interactions between anesthetic drugs on the bispectral index: a preliminary studyPublication . Nunes, Catarina S.; Ferreira, David A.; Mendonça, Teresa F.; Amorim, Pedro; Antunes, Luís M.The aim of this preliminary study was to try to identify the effect of propofol and remifentanil on bispectral index of the electroencephalogram (BIS), and to determine if a model can be generalized between different patients. We used the data from three patients under stable propofol and remifentanil anesthesia for neurosurgical procedures. A Takagi-Sugeno-Kang (TSK) fuzzy model was used to describe the effect of interaction of propofol and remifentanil on BIS. An Adaptive Network-Fuzzy Inference System (ANFIS) was used to obtain the parameters of the TSK model. The ANFIS was able to optimize the parameters of the fuzzy TSK model. The obtained TSK model reflected the data trend and was able to capture the synergistic interaction between propofol and remifentanil during the maintenance phase of anesthesia. The model was trained with the data of two patients and tested with the data of a third patient. Different patients have different physiological responses and this preliminary study results suggested that the BIS values were independent of patients’ variability. This result should be improved using a larger set of data with more patients.
- Performance of blink reflex in patients during anesthesia induction with propofol and remifentanil: prediction probabilities and multinomial logistic analysisPublication . Ferreira, Ana Isabel Leitão; Vide, Sérgio; Felgueiras, João; Cardoso, Márcio; Amorim, Pedro; Mendes, Joaquim; Nunes, Catarina S.The amount of propofol needed to induce loss of responsiveness varied widely among patients, and they usually required less than the initial dose recommended by the drug package inserts. Identifying precisely the moment of loss of responsiveness will determine the amount of propofol each patient needs. Currently, methods to decide the exact moment of loss of responsiveness are based on subjective analysis, and the monitors that use objective methods fail in precision. Based on previous studies, we believe that the blink reflex can be useful to characterize, more objectively, the transition from responsiveness to unresponsiveness. The purpose of this study is to investigate the relation between the electrically evoked blink reflex and the level of sedation/anesthesia measured with an adapted version of the Richmond Agitation-Sedation Scale, during the induction phase of general anesthesia with propofol and remifentanil. Adding the blink reflex to other variables may allow a more objective assessment of the exact moment of loss of responsiveness and a more personalized approach to anesthesia induction.
- Usefulness of the blink reflex to assess the effect of propofol during induction of anesthesia in surgical patientsPublication . Ferreira, Ana Isabel Leitão; Nunes, Catarina S.; Mendes, Joaquim; Amorim, PedroThe aim of this study was to investigate the relation between the blink reflex evoked by an electrical stimulus and the depth of anesthesia induced with intravenous anesthetic drug propofol. The blink reflex was stimulated before the propofol infusion started (baseline) and after, every 6 s. The electromyographic responses and the level of sedation/anesthesia scores as well as the estimated effect-site concentration of propofol were recorded in 11 patients. The blink reflex responses were abolished when patients were still conscious. The clinical scale of anesthesia increased with increasing concentrations of propofol. To predict the level of sedation/anesthesia a multinomial logistic regression was performed using blink reflex extracted features at the frequency domain. Several features proved to be good predictor estimates and the model showed to be useful. This information could be helpful to assess the moment of loss of consciousness and thus personalize anesthesia.