An optimal monitor of the electroencephalographic sigma sleep state

A model has been proposed for a Markov-jumping sleep depth that modulates a white-noise driven structure generating the sigma rhythm in the electroencephalogram. The corresponding maximum likelihood monitor, that continuously detects the current sleep stage from the observed electroencephalogram, has been derived and implemented. Simulations show high detection performances.