title: Searching arousals: A fuzzy logic approach.
abstract: This paper presents a computational approach to detect spontaneous, chin tension and limb movement-related arousals by estimating neuronal and muscular activity. Features extraction is carried out by Time Varying Autoregressive Moving Average (TVARMA) models and recursive particle filtering. Classification is performed by a fuzzy inference system with rule-based decision scheme based upon the AASM scoring rules. Our approach yielded two metrics: arousal density and arousal index to comply with standardised clinical benchmarking. The obtained statistics achieved error deviation around ±1.5 to ±30. These results showed that our system can differentiate amongst 3 different types of arousals, subject to inter-subject variability and up-to-date scoring references.
authors.forename: Ramiro
authors.initials: R
authors.lastname: Chaparro-Vargas
authors.forename: Beena
authors.initials: B
authors.lastname: Ahmed
authors.forename: Thomas
authors.initials: T
authors.lastname: Penzel
authors.forename: Dean
authors.initials: D
authors.lastname: Cvetkovic
mesh_terms: D001143:Arousal; D017143:Fuzzy Logic; D009068:Movement; D009474:Neurons