title: A method for automated temporal knowledge acquisition applied to sleep-related breathing disorders.
abstract: This paper presents a method for the discovery of temporal patterns in multivariate time series and their conversion into a linguistic knowledge representation applied to sleep-related breathing disorders. The main idea lies in introducing several abstraction levels that allow a step-wise identification of temporal patterns. Self-organizing neural networks are used to discover elementary patterns in the time series. Machine learning (ML) algorithms use the results of the neural networks to automatically generate a rule-based description. At the next levels, temporal grammatical rules are inferred. This method covers one of the main "bottlenecks" in the design of knowledge-based systems, namely, the knowledge acquisition problem. An evaluation of the rules lead to an overall sensitivity of 0.762, and a specificity of 0.758.
authors.affiliation: Department of Computer Science and CENTRIA (Centre of AI), Universidade Nova de Lisboa, 2825-114, Caparica, Portugal. gg@di.fct.unl.pt
authors.forename: G
authors.initials: G
authors.lastname: Guimarães
authors.forename: J H
authors.initials: JH
authors.lastname: Peter
authors.forename: T
authors.initials: T
authors.lastname: Penzel
authors.forename: A
authors.initials: A
authors.lastname: Ultsch
mesh_terms: D001185:Artificial Intelligence; D006801:Humans; D016571:Neural Networks, Computer; D010507:Periodicity; D012891:Sleep Apnea Syndromes / Q000503:physiopathology*