Order estimation and model verification in autoregressive modeling of EEG sleep recordings

Use of autoregressive modeling in biomedical signals is widespread. This paper discusses problems associated with finding an optimal model for a signal. Model order criteria and model verification by statistical testing is addressed. The methods are applied on EEG recordings from different sleep stages. It is shown that 5 second EEG segments can be described efficiently by an AR(14) model.