Cyclic variation of heart rate (CVHR) associated with sleep apnea/hypopnea episodes has been suggested as a marker of sleep disordered breathing (SDB). This study examined the utility of ECG-based CVHR detection for diagnosing SDB using simultaneous polysomnography as the reference standard. We used a previously developed automated CVHR detection algorithm (autocorrelated wave detection with adaptive threshold, ACAT) that provides the number of CVHR per hour (CVHR index). The ACAT was refined using a polysomnographic database of 194 subjects with various severities of SDB and then, applied to a single channel ECG obtained during standard overnight polysomnography in 862 consecutive subjects referred for SDB diagnosis. Using multiple thresholds of CVHR index 38 and 27, positive and negative predictive values of 95.6% and 95.1%, respectively, were achieved for detecting and excluding subjects with apnea-hypopnea index (AHI) 30, leaving 58 (6.7%) unclassified subjects. Positive and negative likelihood ratios (LRs) were 97.3 and 0.23, respectively. Also, thresholds of CVHR index 29 and 7 provided 96.1% and 95.1% of positive and negative predictive values, respectively, for subjects with AHI 15 (LRs, 50.6 and 0.11), leaving 426 (49.4%) unclassified subjects. The CVHR correlated with the AHI (r 0.86) and showed the limits of agreement with the AHI of 19.6 and 18.6. Automated detection of CVHR by the ACAT algorithm provides useful screening tool for both increasing and decreasing probability of moderate and sever SDB with adequate thresholds.