title: [Artificial intelligence in sleep analysis (ARTISANA)--modelling visual processes in sleep classification].
abstract: We describe a novel approach to the problem of automated sleep stage recognition. The ARTISANA algorithm mimics the behaviour of a human expert visually scoring sleep stages (Rechtschaffen and Kales classification). It comprises a number of interacting components that imitate the stepwise approach of the human expert, and artificial intelligence components. On the basis of parameters extracted at 1-s intervals from the signal curves, artificial neural networks recognize the incidence of typical patterns, e.g. delta activity or K complexes. This is followed by a rule interpretation stage that identifies the sleep stage with the aid of a neuro-fuzzy system while taking account of the context. Validation studies based on the records of 8 patients with obstructive sleep apnoea have confirmed the potential of this approach. Further features of the system include the transparency of the decision-taking process, and the flexibility of the option for expanding the system to cover new patterns and criteria.
authors: Forschungsbereich Medizinische Informationstechnik, Forschungszentrum Informatik an der Universität Karlsruhe (TH). schwaibold@fzi.de
authors: M
authors: M
authors: Schwaibold
authors: B
authors: B
authors: Schöller
authors: T
authors: T
authors: Penzel
authors: A
authors: A
authors: Bolz
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D005103:Expert Systems; D005260:Female; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D017286:Polysomnography; D015203:Reproducibility of Results; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis* / Q000503:physiopathology; D012894:Sleep Stages / Q000502:physiology*