Diagnosis and decision-making for awareness during general anaesthesia

This is the post-print version of the article. The official published version can be obtained from the link below.We describe the design process of a diagnostic system for monitoring the anaesthetic state of patients during surgical interventions under general anaesthesia. Mid-latency auditory evoked potentials (MLAEPs) obtained during general anaesthesia are used to design a neuro-fuzzy system for the determination of the level of unconsciousness after feature extraction using multiresolution wavelet analysis (MRWA). The neuro-fuzzy system proves to be a useful tool in eliciting knowledge for the fuzzy system: the anaesthetist's expertise is indirectly coded in the knowledge rule-base through the learning process with the training data. The anaesthetic depth of the patient, as deduced by the anaesthetist from the clinical signs and other haemodynamic variables, noted down during surgery, is subsequently used to label the MLAEP data accordingly. This anaesthetist-labelled data, used to train the neuro-fuzzy system, is able to produce a classifier that successfully interprets unseen data recorded from other patients. This system is not limited, however, to the combination of drugs used here. Indeed, the similar effects of inhalational and analgesic anaesthetic drugs on the MLAEPs demonstrate that the system could potentially be used for any anaesthetic and analgesic drug combination. We also suggest the use of a closed-loop architecture that would automatically provide the drug profile necessary to maintain the patient at a safe level of sedation

Towards automation in anaesthesia: a review

Simpósio Internacional MeMeA, realizado em 2014.Surgeries represent a risk for patients and a big cost for the hospital. Anaesthesia represents a complex part of surgery also carries risks for patients. The most known are awareness (with deep psychological consequences); increased risk of morbidity and mortality; adverse reactions and long post-op recovery. The complexity of anaesthesia management can be reduced by studying the patients' responses and developing indicators of the patient state. To assess the level of depth of anaesthesia, the anaesthetist needs to be aware of the patient physiological responses to the drugs and to surgical stimuli. A system that could advise on the patient state considering all clinical signs being measured, the patient individual response and the amount of drugs, will have a big impact on patient overall safety and future health, post-op recovery and hospital resources. This paper does a review of different systems and methods applied to several aspects of the anaesthesia field. All with the goal of working towards automation in this very complex area, that involves high risks for patients. This paper covers advisor systems; signal processing; new monitors and devices; mathematical modelling; and control algorithms; all focused on practical clinical implementation. The objective is to have an overview of the work done so far and the steps taken towards automation in anaesthesia.ISPA - System Integration and Process Automation Unit - Part of the LAETA (Associated Laboratory of Energy, Transports and Aeronautics) a I&D Unit of the Foundation for Science and Technology (FCT), Portugal. FCT support under project PEst-OE/EME/LA0022/2013.info:eu-repo/semantics/publishedVersio

Closed-loop control of anesthesia : survey on actual trends, challenges and perspectives

Automation empowers self-sustainable adaptive processes and personalized services in many industries. The implementation of the integrated healthcare paradigm built on Health 4.0 is expected to transform any area in medicine due to the lightning-speed advances in control, robotics, artificial intelligence, sensors etc. The two objectives of this article, as addressed to different entities, are: i) to raise awareness throughout the anesthesiologists about the usefulness of integrating automation and data exchange in their clinical practice for providing increased attention to alarming situations, ii) to provide the actualized insights of drug-delivery research in order to create an opening horizon towards precision medicine with significantly improved human outcomes. This article presents a concise overview on the recent evolution of closed-loop anesthesia delivery control systems by means of control strategies, depth of anesthesia monitors, patient modelling, safety systems, and validation in clinical trials. For decades, anesthesia control has been in the midst of transformative changes, going from simple controllers to integrative strategies of two or more components, but not achieving yet the breakthrough of an integrated system. However, the scientific advances that happen at high speed need a modern review to identify the current technological gaps, societal implications, and implementation barriers. This article provides a good basis for control research in clinical anesthesia to endorse new challenges for intelligent systems towards individualized patient care. At this connection point of clinical and engineering frameworks through (semi-) automation, the following can be granted: patient safety, economical efficiency, and clinicians' efficacy

Advanced Signal Processing and Control in Anaesthesia

This thesis comprises three major stages: classification of depth of anaesthesia (DOA); modelling a typical patient’s behaviour during a surgical procedure; and control of DOAwith simultaneous administration of propofol and remifentanil. Clinical data gathered in theoperating theatre was used in this project. Multiresolution wavelet analysis was used to extract meaningful features from the auditory evoked potentials (AEP). These features were classified into different DOA levels using a fuzzy relational classifier (FRC). The FRC uses fuzzy clustering and fuzzy relational composition. The FRC had a good performance and was able to distinguish between the DOA levels. A hybrid patient model was developed for the induction and maintenance phase of anaesthesia. An adaptive network-based fuzzy inference system was used to adapt Takagi-Sugeno-Kang (TSK) fuzzy models relating systolic arterial pressure (SAP), heart rate (HR), and the wavelet extracted AEP features with the effect concentrations of propofol and remifentanil. The effect of surgical stimuli on SAP and HR, and the analgesic properties of remifentanil were described by Mamdani fuzzy models, constructed with anaesthetist cooperation. The model proved to be adequate, reflecting the effect of drugs and surgical stimuli. A multivariable fuzzy controller was developed for the simultaneous administration of propofol and remifentanil. The controller is based on linguistic rules that interact with three decision tables, one of which represents a fuzzy PI controller. The infusion rates of the two drugs are determined according to the DOA level and surgical stimulus. Remifentanil is titrated according to the required analgesia level and its synergistic interaction with propofol. The controller was able to adequately achieve and maintain the target DOA level, under different conditions. Overall, it was possible to model the interaction between propofol and remifentanil, and to successfully use this model to develop a closed-loop system in anaesthesia

Control Strategy for Anaesthetic Drug Dosage with Interaction Among Human Physiological Organs Using Optimal Fractional Order PID Controller

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.In this paper, an efficient control strategy for physiological interaction based anaesthetic drug infusion model is explored using the fractional order (FO) proportional integral derivative (PID) controllers. The dynamic model is composed of several human organs by considering the brain response to the anaesthetic drug as output and the drug infusion rate as the control input. Particle Swarm Optimisation (PSO) is employed to obtain the optimal set of parameters for PID/FOPID controller structures. With the proposed FOPID control scheme much less amount of drug-infusion system can be designed to attain a specific anaesthetic target and also shows high robustness for +/-50% parametric uncertainty in the patient's brain model

Performance Analysis of Extracted Rule-Base Multivariable Type-2 Self-Organizing Fuzzy Logic Controller Applied to Anesthesia

We compare type-1 and type-2 self-organizing fuzzy logic controller (SOFLC) using expert initialized and pretrained extracted rule-bases applied to automatic control of anaesthesia during surgery. We perform experimental simulations using a nonfixed patient model and signal noise to account for environmental and patient drug interaction uncertainties. The simulations evaluate the performance of the SOFLCs in their ability to control anesthetic delivery rates for maintaining desired physiological set points for muscle relaxation and blood pressure during a multistage surgical procedure. The performances of the SOFLCs are evaluated by measuring the steady state errors and control stabilities which indicate the accuracy and precision of control task. Two sets of comparisons based on using expert derived and extracted rule-bases are implemented as Wilcoxon signed-rank tests. Results indicate that type-2 SOFLCs outperform type-1 SOFLC while handling the various sources of uncertainties. SOFLCs using the extracted rules are also shown to outperform those using expert derived rules in terms of improved control stability

Fuzzy logic: A “simple” solution for complexities in neurosciences?

Background: Fuzzy logic is a multi-valued logic which is similar to human thinking and interpretation. It has the potential of combining human heuristics into computer-assisted decision making, which is applicable to individual patients as it takes into account all the factors and complexities of individuals. Fuzzy logic has been applied in all disciplines of medicine in some form and recently its applicability in neurosciences has also gained momentum.Methods: This review focuses on the use of this concept in various branches of neurosciences including basic neuroscience, neurology, neurosurgery, psychiatry and psychology.Results: The applicability of fuzzy logic is not limited to research related to neuroanatomy, imaging nerve fibers and understanding neurophysiology, but it is also a sensitive and specific tool for interpretation of EEGs, EMGs and MRIs and an effective controller device in intensive care units. It has been used for risk stratification of stroke, diagnosis of different psychiatric illnesses and even planning neurosurgical procedures.Conclusions: In the future, fuzzy logic has the potential of becoming the basis of all clinical decision making and our understanding of neurosciences