Towards Closing the Loop in Depth-of-Hypnosis Control: Connecting Matlab-Simulink to Medical Devices

Karer, Gorazd

doi:10.1016/j.ifacol.2023.10.012

Cited by 1 publication

(1 citation statement)

References 33 publications

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“…The computation of drug infusion rates in the majority of systems can be classified into one of three methods: manual, open-loop feed-forward, or closed-loop controllers. The conventional method, as seen in figure 6, is the manual system, which entails an anesthesiologist setting the final Depth of Anesthesia (DoA) or Hypnosis (DoH) value and modifying drug dosages based on monitoring the patient's condition [21]. However, the effectiveness of this technique is reliant on the anesthesiologist's proficiency and is not dependable in critical scenarios, such as unexpected incidents during surgery.…”

Section: Controlmentioning

confidence: 99%

A Comprehensive Review on Automated Control of Anesthesia: Recent Methods, Challenges and Future Trends

Toma

Sahib²

2023

wjps

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The safe and personalized administration of anesthetic drugs is a significant concern in clinical practice, and automated control of anesthesia can address this issue by reducing human error, such as under- or over-dosing. This has the added benefit of allowing anesthesiologists to focus on more critical tasks and emergency management. The advantages of automated anesthesia delivery are not limited to anesthesiologists alone, as patients also benefit from the personalized and safe administration of drugs. This article offers a concise overview of the latest developments in closed-loop anesthesia delivery control systems. These systems include a range of elements such as monitoring depth of anesthesia, patient modeling, control techniques, safety systems, and clinical trial validation. Although anesthesia control has undergone significant changes over the years, a fully integrated system remains elusive. To move towards personalized patient care, it is important to assess the current technological limitations, societal considerations, and implementation hurdles, in order to identify new challenges that need to be addressed by intelligent systems. The convergence of clinical and engineering approaches facilitated by automation provides a foundation for research in the field of clinical anesthesia control. This union is crucial to guaranteeing patient safety, cost-effectiveness, and efficient performance by clinicians.

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Section: Controlmentioning

confidence: 99%