Comparison of multiple Kalman filter and moving horizon estimator for the anesthesia process

Aubouin-Pairault, Bob; Fiacchini, Mirko; Dang, Thao

doi:10.1016/j.jprocont.2024.103179

Journal of Process Control

2024

DOI: 10.1016/j.jprocont.2024.103179

|View full text |Cite

Comparison of multiple Kalman filter and moving horizon estimator for the anesthesia process

Bob Aubouin-Pairault,

Mirko Fiacchini,

Thao Dang

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Online identification of pharmacodynamic parameters for closed-loop anesthesia with model predictive control

Aubouin–Pairault,

Fiacchini,

Dang

2024

Computers & Chemical Engineering

View full text Add to dashboard Cite

Online identification of pharmacodynamic parameters for closed-loop anesthesia with model predictive control

Aubouin–Pairault,

Fiacchini,

Dang

2024

Computers & Chemical Engineering

View full text Add to dashboard Cite

Data-driven identification and comparison of full multivariable models for propofol–remifentanil induced general anesthesia

Yumuk,

Copot,

Ionescu

et al. 2024

Journal of Process Control

View full text Add to dashboard Cite

Model Extraction From Clinical Data Subject to Large Uncertainties and Poor Identifiability

Ionescu,

Keyser,

Copot

et al. 2024

IEEE Control Syst. Lett.

View full text Add to dashboard Cite

This letter presents an extension to system theory as a novel approach to provide models from clinical data under large uncertainty and poor identifiability conditions. These difficult conditions are often present in medical systems due to ethical, safety and regulatory limitations regarding application of persistent drug-related excitation to human body. Furthermore, drug-dose effect relationship is of particular challenge due to large interand intra-patient variability. This is strengthened by the lack of suitable instrumentation to measure the necessary information, rather making available inferences and surrogate metrics. A notable advantage of the proposed approach is its robustness to uncertainty. The efficacy of our approach was examined in clinical data from patients monitored during induction phase of target controlled intravenous anesthesia. The proposed method delivered models with physiological explainable parameters and suitable for closed loop control of anesthesia.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Comparison of multiple Kalman filter and moving horizon estimator for the anesthesia process

Cited by 3 publications

References 24 publications

Online identification of pharmacodynamic parameters for closed-loop anesthesia with model predictive control

Online identification of pharmacodynamic parameters for closed-loop anesthesia with model predictive control

Data-driven identification and comparison of full multivariable models for propofol–remifentanil induced general anesthesia

Model Extraction From Clinical Data Subject to Large Uncertainties and Poor Identifiability

Contact Info

Product

Resources

About