title: Neural mass model-based tracking of anesthetic brain states.
abstract: Neural mass model-based tracking of brain states from electroencephalographic signals holds the promise of simultaneously tracking brain states while inferring underlying physiological changes in various neuroscientific and clinical applications. Here, neural mass model-based tracking of brain states using the unscented Kalman filter applied to estimate parameters of the Jansen-Rit cortical population model is evaluated through the application of propofol-based anesthetic state monitoring. In particular, 15 subjects underwent propofol anesthesia induction from awake to anesthetised while behavioral responsiveness was monitored and frontal electroencephalographic signals were recorded. The unscented Kalman filter Jansen-Rit model approach applied to frontal electroencephalography achieved reasonable testing performance for classification of the anesthetic brain state (sensitivity: 0.51; chance sensitivity: 0.17; nearest neighbor sensitivity 0.75) when compared to approaches based on linear (autoregressive moving average) modeling (sensitivity 0.58; nearest neighbor sensitivity: 0.91) and a high performing standard depth of anesthesia monitoring measure, Higuchi Fractal Dimension (sensitivity: 0.50; nearest neighbor sensitivity: 0.88). Moreover, it was found that the unscented Kalman filter based parameter estimates of the inhibitory postsynaptic potential amplitude varied in the physiologically expected direction with increases in propofol concentration, while the estimates of the inhibitory postsynaptic potential rate constant did not. These results combined with analysis of monotonicity of parameter estimates, error analysis of parameter estimates, and observability analysis of the Jansen-Rit model, along with considerations of extensions of the Jansen-Rit model, suggests that the Jansen-Rit model combined with unscented Kalman filtering provides a valuable reference point for future real-time brain state tracking studies. This is especially true for studies of more complex, but still computationally efficient, neural models of anesthesia that can more accurately track the anesthetic brain state, while simultaneously inferring underlying physiological changes that can potentially provide useful clinical information.
authors.affiliation: Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC 3010, Australia. Electronic address: levink@unimelb.edu.au.
authors.forename: Levin
authors.initials: L
authors.lastname: Kuhlmann
authors.affiliation: Department of Medicine - St. Vincent's, University of Melbourne, Parkville, VIC 3010, Australia.
authors.forename: Dean R
authors.initials: DR
authors.lastname: Freestone
authors.affiliation: Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC 3010, Australia.
authors.forename: Jonathan H
authors.initials: JH
authors.lastname: Manton
authors.affiliation: Department of Anesthesia, Ghent University Hospital, Gent, Belgium.
authors.forename: Bjorn
authors.initials: B
authors.lastname: Heyse
authors.affiliation: Department of Anesthesiology, University of Groningen, University Medical Center Groningen, The Netherlands.
authors.forename: Hugo E M
authors.initials: HEM
authors.lastname: Vereecke
authors.affiliation: Department of Information Technology, Pori Campus, Tampere University of Technology, Pori, Finland.
authors.forename: Tarmo
authors.initials: T
authors.lastname: Lipping
authors.affiliation: Department of Anesthesia, Ghent University Hospital, Gent, Belgium; Department of Anesthesiology, University of Groningen, University Medical Center Groningen, The Netherlands.
authors.forename: Michel M R F
authors.initials: MMRF
authors.lastname: Struys
authors.affiliation: Brain and Psychological Sciences Research Centre, Swinburne University of Technology, Hawthorn, VIC, Australia; Cortical Dynamics Ltd, North Perth, WA, Australia.
authors.forename: David T J
authors.initials: DTJ
authors.lastname: Liley
mesh_terms: D000465:Algorithms; D001921:Brain / Q000187:drug effects* / Q000502:physiology*; D003198:Computer Simulation; D056805:Consciousness Monitors; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D006993:Hypnotics and Sedatives / Q000008:administration & dosage; D064795:Intraoperative Neurophysiological Monitoring / Q000379:methods*; D008959:Models, Neurological; D015742:Propofol / Q000008:administration & dosage*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014851:Wakefulness / Q000187:drug effects / Q000502:physiology*
chemical_list: D006993:Hypnotics and Sedatives; D015742:Propofol
keywords: Anesthesia; Brain state tracking; Neural mass models; Parameter estimation; Unscented Kalman filtering