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.

We are witnessing a notable rise in the translational use of information technology and control systems engineering tools in clinical practice. This paper empowers the computer based drug dosing optimization of general anesthesia management by means of multiple variables for patient state stabilization. The patient simulator platform is designed through an interdisciplinary combination of medical, clinical practice and systems engineering expertise gathered in the last decades by our team. The result is an open source patient simulator in Matlab/Simulink from Mathworks(R). Simulator features include complex synergic and antagonistic interaction aspects between general anesthesia and hemodynamic stabilization variables. The anesthetic system includes the hypnosis, analgesia and neuromuscular blockade states, while the hemodynamic system includes the cardiac output and mean arterial pressure. Nociceptor stimulation is also described and acts as a disturbance together with predefined surgery profiles from a translation into signal form of most commonly encountered events in clinical practice. A broad population set of pharmacokinetic and pharmacodynamic (PKPD) variables are available for the user to describe both intra- and inter-patient variability. This simulator has some unique features, such as: i) additional bolus administration from anesthesiologist, ii) variable time-delays introduced by data window averaging when poor signal quality is detected, iii) drug trapping from heterogeneous tissue diffusion in high body mass index patients. We successfully reproduced the clinical expected effects of various drugs interacting among the anesthetic and hemodynamic states. Our work is uniquely defined in current state of the art and first of its kind for this application of dose management problem in anesthesia. This simulator provides the research community with accessible tools to allow a systematic design, evaluation and comparison of various control algorithms for multi-drug dosing optimization objectives in anesthesia.

Cyber-physical systems revolve around context awareness, empowering objective-oriented services, products and operations based on real data. Self-aware and self-control systems are core elements in the Industry 4.0 framework towards self-sustainable adaptive manufacturing and personalized services. This development is witnessed by the context-aware pervasive assistance to users and machines in decisions making process for optimizing product performance and economic yield. While integration of the virtual and the physical world entails smart sensors communication and complex data analytics, it relies on artificial intelligence tools to manage process operations. The objective of the article is to create awareness that systems & control community must address theoretical and practical aspects from a larger perspective. Context aware control is emerging as a natural solution to maximize the use of available sensing instrumentation and the relatively low cost data logging, i.e. an important source for extracting information, interpreting and using context information and adapt its functionality to the current context of use. This article presents a concise overview of applications where context aware systems and control methodologies are relevant in the seven societal challenges acknowledged by European policy-makers: Digital Society; Food; Health and Well-Being; Smart Resource Management; Urban Planning, Mobility Dynamics and Logistics; New Energy Demand and Delivery; and Society.

The paper aims to revive the interest in bioimpedance analysis for pain studies in communicating and non-communicating (anesthetized) individuals for monitoring purpose. The plea for exploitation of full potential offered by the complex (bio)impedance measurement is emphasized through theoretical and experimental analysis. A non-invasive, low-cost reliable sensor to measure skin impedance is designed with off-the-shelf components. This is a second generation prototype for pain detection, quantification, and modeling, with the objective to be used in fully anesthetized patients undergoing surgery. The 2D and 3D time–frequency, multi-frequency evaluation of impedance data is based on broadly available signal processing tools. Furthermore, fractional-order impedance models are implied to provide an indication of change in tissue dynamics correlated with absence/presence of nociceptor stimulation. The unique features of the proposed sensor enhancements are described and illustrated here based on mechanical and thermal tests and further reinforced with previous studies from our first generation prototype.

Fractional calculus has been opening new doors in terms of better modeling and control of several phenomena and processes. Biomedical engineering has seen a lot of combined attention from clinicians, control engineers and researchers in their attempt to offer individualized treatment. A large number of medical procedures require anesthesia, which in turn requires a closely monitored and controlled level of hypnosis, analgesia and neuromuscular blockade, as well maintenance of hemodynamic variables in a safe range. Computer-controlled anesthesia has been given a tremendous amount of attention lately. Hemodynamic stabilization via computer-based control is also a hot topic. However, very few studies on automatic control of combined anesthesia–hemodynamic systems exist despite the fact that hemodynamics is strongly influenced by hypnotic drugs, while the depth of hypnosis is affected by drugs used in hemodynamic control. The very first multivariable fractional-order controller is developed in this paper for the combined anesthesia–hemodynamic system. Simulation studies on 24 patients show the effectiveness of the proposed approach.

Although the measurement of dielectric properties of the skin is a long-known tool for assessing the changes caused by nociception, the frequency modulated response has not been considered yet. However, for a rigorous characterization of the biological tissue during noxious stimulation, the bioimpedance needs to be analyzed over time as well as over frequency. The 3-dimensional analysis of nociception, including bioimpedance, time, and frequency changes, is provided by ANSPEC-PRO device. The objective of this observational trial is the validation of the new pain monitor, named as ANSPEC-PRO. After ethics committee approval and informed consent, 26 patients were monitored during the postoperative recovery period: 13 patients with the in-house developed prototype ANSPEC-PRO and 13 with the commercial device MEDSTORM. At every 7 min, the pain intensity was measured using the index of Anspec-pro or Medstorm and the 0–10 numeric rating scale (NRS), pre-surgery for 14 min and post-anesthesia for 140 min. Non-significant differences were reported for specificity-sensitivity analysis between ANSPEC-PRO (AUC = 0.49) and MEDSTORM (AUC = 0.52) measured indexes. A statistically significant positive linear relationship was observed between Anspec-pro index and NRS (r² = 0.15, p < 0.01). Hence, we have obtained a validation of the prototype Anspec-pro which performs equally well as the commercial device under similar conditions.