Sleep Apnea Syndrome Identification Using Hidden Markov Models

In this work, a sleep apnea diagnosis system based on Hidden Markov Models (HMMs) is presented. Conventional and new simulated annealing based methods for the training of HMMs are incorporated. The inference method of this system translates parameter values into interpretations of physiological and pathophysiological states. The interpretation is extended to sequences of states in time to obtain a state-space trajectory. The measurements of the respiratory activity issued by the technique of polysomnography (upper airway flow, esophageal pressure and gastric pressure) are considered for off-line and on-line detection of the different sleep apnea syndromes: obstructive, central and hypopnea. Experimental results using respiratory clinical data are presented.