The thesis deals with the study and automatic analysis of the sleep. The underlying assumption is that sleep is a highly structured dynamic process, its temporal organization being modeled and analysed in terms of stochastic context-free grammars. A syntactic model is proposed for the analysis of sleep macrostructure, as given by the hypno gram; this model is tested in the comparison of a population of normals with a population of psychiatric patients. Concerning the microstructure, only the sleep electroencephalogram (EEG) is considered, the purpose being to predict the entrance in the Rem (rapid Eye Movements) stage from stage 2NREM. A hierarchical model is proposed comprising, at a first level, feature extraction provided by a stochastic model of sleep EEG and, at a higher level, syntactic models. The above problems determined the development of a general methodological and algorithmic framework in the domain of structural pattern recognition. Two heuristic based parsing / recognition algorithms for general stochastic context-free grammars are introduced. Also, efficient algorithms were developed for the computation of sub-string probabilities according to a subclass of context-free grammars which naturally arise when inferring grammars with Crespi-Reghizzi's method based on samples with a temporal structure. For the automatic production of structural samples, to be used in grammatical inference, a hybrid methodology using Solomon off coding is proposed. The method consists of devising sub patterns in the training sequences by means of Solomon off coding, and using these as priori information for constraining the production of structural samplesAvailable from Fundacao para a Ciencia e a Tecnologia, Servico de Informacao e Documentacao, Av. D. Carlos I, 126, 1249-074 Lisboa, Portugal / FCT - Fundação para o Ciência e a TecnologiaSIGLEPTPortuga
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.