Fuzzy structural algorithms to identify and characterize apnea and hypopnea episodes

We present a new automated method to identify apneas and hypopneas and to relate them to the drops in blood oxyhemoglobin saturation that they produce. The algorithm takes advantage of the fuzzy set theory for the representation and manipulation of the vagueness of the medical knowledge on which it is based. Its structural nature allows us to easily modify the morphological detection criteria, and to perform a detailed characterization of the identified events. Based on this proposal, a tool for screening polysomnographic records has been developed. The tool allows the physician to modify the morphological criteria that define apneas, hypopneas, and desaturations as well as to inspect the descriptors that make up their characterization. Using this tool we have analyzed five polysomnographic recordings obtaining an overall correct detection rate of 95%. The false negative rate was 6.6% and the false positive rate was 4.3%.

keywords: Sleep Apnea-Hypopnea Syndrome (SAHS), Biosignal Processing, Structural Pattern Recognition, Fuzzy Set Theory.