Method of characterizing sleep disordered breathing

Abstract

A method of characterizing a patient's disordered breathing during a sleeping period includes performing a first partial characterization of a time axis of an audio signal in order to learn the most prominent and highly relevant events. Only at a later stage, i.e., after sufficient observation of the highly relevant events, is a full segmentation of the entire time axis actually carried out. Linear prediction is used to create an excitation signal that is employed to provide better segmentation than would be possible using the original audio signal alone. Warped linear prediction or Laguerre linear prediction is employed to create an accurate spectral representation with flexibility in the details provided in different frequency ranges. A resonance probability function is generated to further characterize the signals in order to identify disordered breathing. An output includes a characterization in any of a variety of forms of identified disordered breathing.