  Sleep apnea is the most common sleep disturbance and it is an important risk
factor for cardiovascular disorders. Its detection relies on a polysomnography,
a combination of diverse exams.
  In order to detect changes due to sleep disturbances such as sleep apnea
occurrences, without the need of combined recordings, we mainly analyze
systolic blood pressure signals (maximal blood pressure value of each beat to
beat interval). Nonstationarities in the data are uncovered by a segmentation
procedure, which provides local quantities that are correlated to
apnea-hypopnea events. Those quantities are the average length and average
variance of stationary patches. By comparing them to an apnea score previously
obtained by polysomnographic exams, we propose an apnea quantifier based on
blood pressure signal.
  This furnishes an alternative procedure for the detection of apnea based on a
single time series, with an accuracy of 82%.
