title: Sleep classification in infants by decision tree-based neural networks.
abstract: This paper presents an AI-based approach to automatic sleep stage scoring. The system TBNN (Tree-Based Neural Network) uses a decision-tree generator to provide knowledge that defines the architecture of a backpropagation neural network, including feature selection and initialisation of the weights. The case study reports a successful application to the data from polygraphic all-night sleep of 8 babies aged 6 months. The teaching input was provided by a medical expert in accordance with the rules of Guilleminault and Souquet. The performance of TBNN is compared with 5 other methods and the results are discussed.
authors.affiliation: Ludwig Boltzmann Institute of Medical Informatics and Neuroinformatics, Graz, Austria. irena@iinf.bg
authors.forename: I
authors.initials: I
authors.lastname: Koprinska
authors.forename: G
authors.initials: G
authors.lastname: Pfurtscheller
authors.forename: D
authors.initials: D
authors.lastname: Flotzinger
mesh_terms: D001185:Artificial Intelligence; D003625:Data Collection; D003663:Decision Trees; D017143:Fuzzy Logic; D006801:Humans; D007223:Infant; D016571:Neural Networks, Computer; D017286:Polysomnography; D012890:Sleep