Nonlinear analysis is widely used in the study of electroencephalographic signals. Nonlinear prediction by means of radial basis functions can be used to model the signals dynamics and acquire information about the system under study. We propose a new method in order to improve the prediction performance of the radial basis functions model: the basis functions are modified using a locally estimated statistics of the dynamics. This approach allows using a small number of degrees of freedom in the model, preserving its generalization properties. The method was tested on EEG signals acquired during sleep stages with and without apnea events.

Nonlinear Prediction of EEG Signals with Radial Basis Fucntions to Model Sleep Apnea Events

VANELLO, NICOLA;LANDINI, LUIGI
2004-01-01

Abstract

Nonlinear analysis is widely used in the study of electroencephalographic signals. Nonlinear prediction by means of radial basis functions can be used to model the signals dynamics and acquire information about the system under study. We propose a new method in order to improve the prediction performance of the radial basis functions model: the basis functions are modified using a locally estimated statistics of the dynamics. This approach allows using a small number of degrees of freedom in the model, preserving its generalization properties. The method was tested on EEG signals acquired during sleep stages with and without apnea events.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/89594
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