Introduction: Despite its severe cardiovascular and other consequences, sleep apnea syndrome frequently is undiagnosed. Because apneas result in repeated autonomic arousals associated with cyclic variations in heart rate (CVHR), we hypothesized that sleep apnea syndrome could be identified from simple HR tachograms (graphs of HR vs time) derived from ECG monitoring.
Methods and results: HR tachograms were generated from 57 digitized ECGs (46 clinical patients undergoing diagnostic studies and 11 research subjects) obtained during overnight polysomnography. Thirty-three had significant sleep apnea syndrome (apnea-hypopnea index > or = 15). Eight patients had simultaneous Holter recordings during sleep studies (3 with digitized ECGs and 5 with paper ECGs). Duration of CVHR on tachograms was determined. CVHR patterns were characterized as high amplitude (HR changes > or = 20 beats/min per cycle) versus lower amplitude (6-19 beats/min per cycle); or regular (in frequency, amplitude, and morphology) versus irregular. Tachograms were classified as having visible HR changes versus not visible (flat). Twenty-four studies proved to be split-night, so CVHR was quantified for the first 3 hours of each study only. When subjects were dichotomized into shorter (< 20%, < 36 min) and longer (> or = 20%) durations of CVHR, longer CVHR had a positive predictive accuracy of 86% for significant sleep apnea syndrome and 100% for abnormal sleep. When flat tachograms were excluded, negative predictive accuracy for shorter CVHR was 100%. All patients (N = 13) with > 36 min high-amplitude CVHR had significant obstructive sleep apnea. All predictions from Holter-only data were concordant with clinical diagnoses.
Conclusion: HR tachogram patterns derived from ambulatory ECGs provide a simple method for identifying sleep apnea syndrome and other sleep disturbances in patients without major autonomic dysfunction.