Classification of sleep-waking states using modular neural networks

Applies modular neural network models to classify sleep-waking states in infants. The performances of three connectionist models are compared: (a) a multilayer perceptron (MLP), (b) a mixture of experts (ME) and (c) a fuzzy ganglionar lattice (FGL). We propose a new methodology for enhancing neural classifiers based on input variable selection and confusion error analysis using expert criteria. The ME model was more robust than the MLP and FGL models in the presence of inconsistent or noisy data. Input variable selection and confusion error analysis using expert criteria led to parsimonious models with less parameters and better classification rates.