BACKGROUND: Despite the adverse cardiovascular consequences of obstructive sleep apnea, the majority of patients remain undiagnosed. To explore an efficient ECG-based screening tool for obstructive sleep apnea, we examined the usefulness of automated detection of cyclic variation of heart rate (CVHR) in a large-scale controlled clinical setting. METHODS AND RESULTS: We developed an algorithm of autocorrelated wave detection with adaptive threshold (ACAT). The algorithm was optimized with 63 sleep studies in a training cohort, and its performance was confirmed with 70 sleep studies of the Physionet Apnea-ECG database. We then applied the algorithm to ECGs extracted from all-night polysomnograms in 862 consecutive subjects referred for diagnostic sleep study. The number of CVHR per hour (the CVHR index) closely correlated (r=0.84) with the apnea-hypopnea index, although the absolute agreement with the apnea-hypopnea index was modest (the upper and lower limits of agreement, 21 per hour and -19 per hour) with periodic leg movement causing most of the disagreement (P<0.001). The CVHR index showed a good performance in identifying the patients with an apnea-hypopnea index ≥15 per hour (area under the receiver-operating characteristic curve, 0.913; 83% sensitivity and 88% specificity, with the predetermined cutoff threshold of CVHR index ≥15 per hour). The classification performance was unaffected by older age (≥65 years) or cardiac autonomic dysfunction (SD of normal-to-normal R-R intervals over the entire length of recording <65 ms; area under the receiver-operating characteristic curve, 0.915 and 0.911, respectively). CONCLUSIONS: The automated detection of CVHR with the ACAT algorithm provides a powerful ECG-based screening tool for moderate-to-severe obstructive sleep apnea, even in older subjects and in those with cardiac autonomic dysfunction.
BACKGROUND: Despite the adverse cardiovascular consequences of obstructive sleep apnea, the majority of patients remain undiagnosed. To explore an efficient ECG-based screening tool for obstructive sleep apnea, we examined the usefulness of automated detection of cyclic variation of heart rate (CVHR) in a large-scale controlled clinical setting. METHODS AND RESULTS: We developed an algorithm of autocorrelated wave detection with adaptive threshold (ACAT). The algorithm was optimized with 63 sleep studies in a training cohort, and its performance was confirmed with 70 sleep studies of the Physionet Apnea-ECG database. We then applied the algorithm to ECGs extracted from all-night polysomnograms in 862 consecutive subjects referred for diagnostic sleep study. The number of CVHR per hour (the CVHR index) closely correlated (r=0.84) with the apnea-hypopnea index, although the absolute agreement with the apnea-hypopnea index was modest (the upper and lower limits of agreement, 21 per hour and -19 per hour) with periodic leg movement causing most of the disagreement (P<0.001). The CVHR index showed a good performance in identifying the patients with an apnea-hypopnea index ≥15 per hour (area under the receiver-operating characteristic curve, 0.913; 83% sensitivity and 88% specificity, with the predetermined cutoff threshold of CVHR index ≥15 per hour). The classification performance was unaffected by older age (≥65 years) or cardiac autonomic dysfunction (SD of normal-to-normal R-R intervals over the entire length of recording <65 ms; area under the receiver-operating characteristic curve, 0.915 and 0.911, respectively). CONCLUSIONS: The automated detection of CVHR with the ACAT algorithm provides a powerful ECG-based screening tool for moderate-to-severe obstructive sleep apnea, even in older subjects and in those with cardiac autonomic dysfunction.
Authors: Kenneth E Freedland; Robert M Carney; Junichiro Hayano; Brian C Steinmeyer; Rebecca L Reese; Annelieke M Roest Journal: J Psychosom Res Date: 2012-01-28 Impact factor: 3.006
Authors: M Melani Lyons; Jan F Kraemer; Radha Dhingra; Brendan T Keenan; Niels Wessel; Martin Glos; Thomas Penzel; Indira Gurubhagavatula Journal: J Clin Sleep Med Date: 2019-01-15 Impact factor: 4.062
Authors: Junichiro Hayano; Robert M Carney; Eiichi Watanabe; Kiyohiro Kawai; Itsuo Kodama; Phyllis K Stein; Lana L Watkins; Kenneth E Freedland; James A Blumenthal Journal: Psychosom Med Date: 2012-09-28 Impact factor: 4.312
Authors: Jacek Wolf; Jacek Drozdowski; Krzysztof Czechowicz; Paweł J Winklewski; Ewa Jassem; Tomas Kara; Virend K Somers; Krzysztof Narkiewicz Journal: Int J Cardiol Date: 2015-08-21 Impact factor: 4.164