Individualized PID control of depth of anesthesia based on patient model identification during the induction phase of anesthesia

Soltesz, Kristian; Hahn, Jin-Oh; Dumont, Guy A.; Ansermino, J. Mark

doi:10.1109/cdc.2011.6160189

Cited by 17 publications

(15 citation statements)

References 12 publications

(18 reference statements)

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“…This observer, together with the control law proposed in Nogueira et al (2014), was tested in real clinical cases, and the corresponding results are presented here. These results encourage the use of our observer-controller scheme for the control of the depth of anesthesia, a problem that has lately deserved much attention (Hemmerling et al (2010), Ionescu et al (2008), Dumont (2012), Furutani et al (2005), Soltesz et al (2011)). …”

Section: Model Descriptionmentioning

confidence: 57%

Automatic control of the depth of anesthesia - clinical results

2015

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Section: Model Descriptionmentioning

confidence: 57%

Automatic control of the depth of anesthesia - clinical results

2015

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“…Nevertheless, the physiological parameters of patients vary based on age, weight, disease, and type of surgery being performed, and presently the available patient data is limited and does not adequately depict the physiological parameters of all patients. Therefore, online identification of patient parameters can be useful for improving the controller performance [268]. Additional studies have shown that the use of gain scheduling techniques may also be beneficial.…”

Section: Automation In Anesthesiamentioning

confidence: 99%

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

et al. 2020

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Over the past decade, there has been an unprecedented international focus on improved quality and availability of medical care, which has reignited interest in clinical automation and drawn researchers toward novel solutions in the field of physiological closed-loop control systems (PCLCs). Today, multidisciplinary groups of expert scientists, engineers, clinicians, mathematicians, and policy-makers are combining their knowledge and experience to develop both the next generation of PCLC-based medical equipment and a collaborative commercial/academic infrastructure to support this rapidly expanding frontier. In the following article, we provide a robust introduction to the various aspects of this growing field motivated by the recent and ongoing work supporting two leading technologies: the artificial pancreas (AP) and automated anesthesia. Following a brief high-level overview of the main concepts in automated therapy and some relevant tools from systems and control theory, we explore -separately -the developments, challenges, state-ofthe-art, and probable directions for AP and automated anesthesia systems. We then close the review with a consideration of the common lessons gleaned from these ventures and the implications they present for future investigations and adjacent research.

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“…Results of a feasibility study in adults using the NeuroSENSE DOH monitor (NeuroWave Systems Inc., Cleveland Heights, OH) showed that this simple controller provided clinically adequate anesthesia, 55 with the control variable within the target range 88% of the time and a median time to induction of 4 minutes. 59 Sawaguchi et al 60 describe a model-predictive controller-based system for control of DOH that updates the patient-based model on induction data and is augmented by a set of rule-based fallback procedures. The results of a pilot study are reported in Soltesz et al, 56 van Heusden et al, 57 and West et al, 58 with the control variable within the target range 89% of the time and a median induction time of 3.8 minutes.…”

Section: Clinical Studiesmentioning

confidence: 99%

Closed-Loop Control of Anesthesia

Dumont¹,

Ansermino²

2013

Anesthesia &Amp; Analgesia

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Feedback control is ubiquitous in nature and engineering and has revolutionized safety in fields from space travel to the automobile. In anesthesia, automated feedback control holds the promise of limiting the effects on performance of individual patient variability, optimizing the workload of the anesthesiologist, increasing the time spent in a more desirable clinical state, and ultimately improving the safety and quality of anesthesia care. The benefits of control systems will not be realized without widespread support from the health care team in close collaboration with industrial partners. In this review, we provide an introduction to the established field of control systems research for the everyday anesthesiologist. We introduce important concepts such as feedback and modeling specific to control problems and provide insight into design requirements for guaranteeing the safety and performance of feedback control systems. We focus our discussion on the optimization of anesthetic drug administration.

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Individualized PID control of depth of anesthesia based on patient model identification during the induction phase of anesthesia

Cited by 17 publications

References 12 publications

Automatic control of the depth of anesthesia - clinical results

Automatic control of the depth of anesthesia - clinical results

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

Closed-Loop Control of Anesthesia

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