Fractional order impedance models as rising tools for quantification of unconscious analgesia

This research focuses on modeling the diffusion process that occurs in the human body when an analgesic drug is taken up, by using fractional-order impedance models (FOIMs). We discuss the measurement of a suitable feedback signal that can be used in a model-based control strategy. With this knowledge an early dawn concept of a pain sensor is presented. The major challenges that are encountered during this development consist of identification of the patient model, validation of the pain sensor and validation of the effect of the analgesic drug

PID control of depth of hypnosis in anesthesia for propofol and remifentanil coadministration

Tese de mestrado, Engenharia Biomédica e Biofísica, 2022, Universidade de Lisboa, Faculdade de CiênciasThe purpose of general anesthesia is to deeply sedate a person so that they lose consciousness, sensitivity, and body reflexes, and so that surgeries can be safely performed without the patient feeling pain or discomfort during the procedure. General anesthesia is a combination of the effect of three components, namely hypnosis, analgesia, and neuromuscular blockade. Each component is regulated through the action of a specific drug, or through the combined effect of two or more drugs. In recent years there have been many advances in the field of automatic control systems for drug delivery during anesthesia, which can be implemented using a wide variety of controllers and process variables. The reason behind these advances is that an automatic control system can provide several benefits, such as a reduction in the anesthesiologist's workload, a reduction in the amount of medication used (which implies a faster and better recovery time for the patient in the postoperative phase), and, in fact, a more robust performance with fewer episodes of over- or under-dosing of the drug. A proportional-integral-derivative controller (PID) continuously calculates the error value that is the difference between the desired value and the measured process variable and applies a correction that is based on proportional, integral and derivative terms. In this dissertation, a specific PID control system for propofol and remifentanil is proposed to regulate the hypnosis component during anesthesia using the bispectral index (BIS) as the process variable. Infusion rates of both drugs are also controlled. The adjustment of the PID parameters, so that the BIS was closer to what was expected, was done using a genetic algorithm. The implementation of the control system was done in Simulink in order to simulate a surgery. The simulation scheme includes the patient models for both drugs, a disturbance profile, and two different PID controllers for the two phases of anesthesia - induction and maintenance. Aspects such as noise in the BIS signal and artifacts were taken into account in the system and a suitable noise filter was applied in the control algorithm. In addition, a ratio between the infusion rates of propofol and remifentanil has been introduced to allow the anesthesiologist to choose the appropriate opioid-hypnotic balance In the end, a performance analysis of the control system was made based on seven performance indices (namely the integrated absolute error, the settling time, the median performance error, the median absolute performance error, the wobble, and the above and below recommended BIS values). Although there are many types of control systems for the automatic control of hypnosis depth described in the literature, these are not usually used in clinical practice. Therefore, it is important to continue research to produce robust and user-friendly systems that integrate clinicians' clinical knowledge and meet their actual needs

A computationally efficient Hill curve adaptation strategy during continuous monitoring of dose-effect relation in anaesthesia

This paper discusses a possibility to simplify the number of parameters in the Hill curve by exploiting special mathematical functions. This simplification is relevant when adaptation is required for personalized model-based medicine during continuous monitoring of dose-response values. A mathematical framework of the involved physiology and modelling by means of distributed parameter progressions has been employed. Convergence to a unique dynamic response is achieved, allowing simplifying assumptions with guaranteed solution. Discussion on its use and comparison with other adaptation mechanism is provided

Detection and evaluation of events in EEG dynamics in post-surgery patients with physiological-based mathematical models

As part of the new directions for vision and mission of Europe, patient well-being and healthcare become core features of a modern and prosperous society. That is, healthcare costs are optimized towards patient benefit and sideways effects such as cost-related reduction in medication, in frequency of post-operatory interventions, in recovery times and in comorbidity risk. In this paper, we address the incidence of events related to stroke, epileptic seizures and tools to possibly predict their presence from Electroencephalography (EEG) signal acquired in post-surgery patients. Wavelet analysis and spectrogram indicate graphically changes in the energy content of the EEG signal. Physiologically based neuronal dynamic pathway is used to derive fractional order impedance models. Nonlinear least squares identification technique is used to identify model parameters, with results suggesting parameter redundancy. There is a significant difference in model parameter values between EEG signal with/-out events