Detection of obstructive sleep apnea from cardiac interbeat interval time series

We present a new automated method to diagnose and quantify obstructive sleep apnea from single-lead electrocardiograms based on the detection of the periodic oscillations in cardiac interbeat intervals that are often associated with prolonged cycles of sleep apnea. This technique employs the Hilbert transformation of the sinus interbeat interval time series to derive the instantaneous amplitudes and frequencies of the series and calculates their averages and standard deviations over a moving 5-minute window. We then apply a thresholding technique and detect continuous sequences of those windows that lie within threshold limits. When applied to the Computers in Cardiology sleep apnea rest data, our algorithm correctly classified 28 out of 30 cases (93.3%) of both sleep apnea and normal subjects, and correctly identified the presence or absence of sleep apnea in 14591 out of a total of 17268 minutes (84.5%) of the data from the test set.