A method for automated temporal knowledge acquisition applied to sleep-related breathing disorders.
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.
Guimarães
G
G
Department of Computer Science and CENTRIA (Centre of AI), Universidade Nova de Lisboa, 2825-114, Caparica, Portugal. gg@di.fct.unl.pt
Peter
J H
JH
Penzel
T
T
Ultsch
A
A
D001185:Artificial Intelligence; D006801:Humans; D016571:Neural Networks, Computer; D010507:Periodicity; D012891:Sleep Apnea Syndromes / Q000503:physiopathology*