Validation of ECG-derived sleep architecture and ventilation in sleep apnea and chronic fatigue syndrome
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Newly developed algorithms putatively derive measures of sleep, wakefulness, and respiratory disturbance index (RDI) through detailed analysis of heart rate variability (HRV). Here, we establish levels of agreement for one such algorithm through comparative analysis of HRV-derived values of sleep–wake architecture and RDI with those calculated from manually scored polysomnographic (PSG) recordings.
Archived PSG data collected from 234 subjects who participated in a 3-day, 2-night study characterizing polysomnographic traits of chronic fatigue syndrome were scored manually. The electrocardiogram and pulse oximetry channels were scored separately with a novel scoring algorithm to derive values for wakefulness, sleep architecture, and RDI.
Four hundred fifty-four whole-night PSG recordings were acquired, of which, 410 were technically acceptable. Comparative analyses demonstrated no difference for total minutes of sleep, wake, NREM, REM, nor sleep efficiency generated through manual scoring with those derived through HRV analyses. When NREM sleep was further partitioned into slow-wave sleep (stages 3–4) and light sleep (stages 1–2), values calculated through manual scoring differed significantly from those derived through HRV analyses. Levels of agreement between RDIs derived through the two methods revealed an R = 0.89. The Bland–Altman approach for determining levels of agreement between RDIs generated through manual scoring with those derived through HRV analysis revealed a mean difference of −0.7 ± 8.8 (mean ± two standard deviations).
We found no difference between values of wakefulness, sleep, NREM, REM sleep, and RDI calculated from manually scored PSG recordings with those derived through analyses of HRV.
KeywordsHeart rate variability Electrocardiogram Sleep apnea Sleep architecture Respiratory disturbance index Validation
The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the funding agency.
The author(s) declare that Michael Decker, Clement Cahan, and William Reeves have no competing interests. Shulamit Eyal, Zvika Shinar, Yair Fuxman, and Anda Baharav were employees or shareholders of Hypnocore during the time of this study.
- 16.Toledo E, Gurevitz O, Hod H, Eldar M, Akselrod S (1998) The use of a wavelet transform for the analysis of nonstationaryheart rate variability signal during thrombolytic therapy as a marker of reperfusion. Comput Cardiol 1998:609–612Google Scholar
- 17.Shinar Z, Baharav A, Dagan Y, Akselrod S (2001) Automatic detection of slow-wave-sleep using heart rate variability. Comput Cardiol 2001:593–596Google Scholar
- 21.Moody G, Mark R, Zoccola A, Mantero S (1985) Derivation of respiratory signals from multi-lead ECGs. Comput Cardiol 12:113–116Google Scholar
- 28.Fleisher LA, Frank SM, Sessler DI, Cheng C, Matsukawa T, Vannier CA (1996) Thermoregulation and heart rate variability. Clin Sci (Lond) 90(2):97–103Google Scholar
- 33.Collop NA, Anderson WM, Boehlecke B, Claman D, Goldberg R, Gottlieb DJ et al (2007) Clinical guidelines for the use of unattended portable monitors in the diagnosis of obstructive sleep apnea in adult patients. Portable monitoring task force of the American Academy of Sleep Medicine. J Clin Sleep Med 3(7):737–747PubMedGoogle Scholar