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Estimation of Total Sleep Time From Respiratory Event Intervals in Sleep Disordered Breathing
Luca Cerina1, Gabriele B Papini2, Sebastiaan Overeem1,3
1Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.
Journal of Sleep Research
|July 16, 2026
Summary
A new method estimates total sleep time (TST) using only respiratory event data and total recording time (TRT). This improves the accuracy of sleep apnea severity assessment in home-based sleep studies.
Area of Science:
- Sleep Medicine
- Respiratory Physiology
- Biomedical Engineering
Background:
- Accurate estimation of total sleep time (TST) and respiratory events is crucial for diagnosing sleep-disordered breathing (SDB) using polysomnography (PSG).
- Home-based polygraphy (PG) often lacks TST, leading to the use of the respiratory event index (REI) based on total recording time (TRT), which can underestimate SDB severity due to sleep fragmentation.
Purpose of the Study:
- To develop and validate a device-agnostic method for estimating TST using only respiratory event intervals and TRT.
- To improve the accuracy of REI calculations in home-based sleep studies, particularly for individuals with fragmented sleep.
Main Methods:
- Proposed a novel method to estimate TST by analyzing time intervals between respiratory events and TRT.
- Employed bootstrap sampling to quantify REI uncertainty and a regression model integrating demographic and TST-independent sleep metrics for TST refinement.
- Validated the approach on 2037 recordings from the MESA cohort.
Main Results:
- Achieved a moderately good estimation of TST (Correlation 0.84, agreement ±44.5 min).
- Demonstrated improved TST-based REI compared to TRT-based REI, with a 70.3% error reduction and 8x fewer false negatives, even with low sleep efficiency.
- The method's performance was comparable to actigraphy-based methods and improved when combined, though it underperformed compared to methods using cardio-respiratory signals.
Conclusions:
- It is feasible to obtain a moderate-to-good TST estimate using minimal information from respiratory events and TRT.
- The proposed method enhances existing REI estimates without requiring additional physiological signals, offering a valuable tool for home-based sleep studies.
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