title: Modeling for neuromonitoring depth of anesthesia.
abstract: This article reviews the various modeling techniques for neuromonitoring depth of anesthesia (DOA). Traditional techniques such as parametric, predictive, optimal, and adaptive modeling, proportional, integral, derivative (PID) modeling, together with modern techniques such as bispectral-based, artificial neural-network-based, fuzzy logic, and neuro-fuzzy modeling, bring us to the current state of the art in DOA neuromonitoring. This article reviews historical information about each of the modern techniques and provides an example demonstrating its implementation; reviews drug pharmacokinetic/pharmacodynamic (PK/PD) and drug interaction PK/PD modeling techniques for a balanced total intravenous anesthesia (TIVA) administration; and discusses the existing technical problems and clinical challenges, suggesting new techniques necessary for the future development of a DOA monitoring and control system.
authors.affiliation: Siemens Medical Solutions USA, Inc., Danvers, Massachusetts, USA.
authors.forename: Xu-Sheng
authors.initials: XS
authors.lastname: Zhang
authors.forename: Johnnie W
authors.initials: JW
authors.lastname: Huang
authors.forename: Rob J
authors.initials: RJ
authors.lastname: Roy
mesh_terms: D000758:Anesthesia / Q000379:methods*; D000818:Animals; D004285:Dogs; D017143:Fuzzy Logic; D006801:Humans; D008959:Models, Neurological; D016343:Monitoring, Intraoperative / Q000379:methods*; D008991:Monitoring, Physiologic / Q000379:methods / Q000639:trends; D011237:Predictive Value of Tests; D051381:Rats