Currently all respiratory events are identified manually and totaled. This is time consuming and subject to variability. The objective of the present project is to improve upon the manual approach by implementing an artificially intelligent system for the identification and quantification of sleep disordered breathing based solely on non-invasive cardiopulmonary signals collected during a routine sleep study. The utility of other reported indices of sleep disordered breathing obtained during a sleep study will be evaluated.