Overnight Sleep Staging Using Chest-Worn Accelerometry

Overnight sleep staging is an important part of the diagnosis of various sleep disorders. Polysomnography is the gold standard for sleep staging, but less-obtrusive sensing modalities are of emerging interest. Here, we developed and validated an algorithm to perform "proxy" sleep staging u...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-09, Vol.24 (17), p.5717
Main Authors: Schipper, Fons, Grassi, Angela, Ross, Marco, Cerny, Andreas, Anderer, Peter, Hermans, Lieke, van Meulen, Fokke, Leentjens, Mickey, Schoustra, Emily, Bosschieter, Pien, van Sloun, Ruud J G, Overeem, Sebastiaan, Fonseca, Pedro
Format: Article
Language:English
Subjects:
accelerometer
Accelerometers
Accelerometry - instrumentation
Accelerometry - methods
Adolescent
Adult
Aged
Aged, 80 and over
Algorithms
Artificial intelligence
Clocks & watches
Data collection
Electroencephalography
Electromyography
Female
Heart rate
Humans
hypnogram
Male
Medical research
Middle Aged
Neural networks
Polysomnography - methods
Sensors
Sleep apnea
Sleep disorders
sleep metrics
Sleep Stages - physiology
sleep staging
Thorax
Time series
Young Adult
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Summary:Overnight sleep staging is an important part of the diagnosis of various sleep disorders. Polysomnography is the gold standard for sleep staging, but less-obtrusive sensing modalities are of emerging interest. Here, we developed and validated an algorithm to perform "proxy" sleep staging using cardiac and respiratory signals derived from a chest-worn accelerometer. We collected data in two sleep centers, using a chest-worn accelerometer in combination with full PSG. A total of 323 participants were analyzed, aged 13-83 years, with BMI 18-47 kg/m . We derived cardiac and respiratory features from the accelerometer and then applied a previously developed method for automatic cardio-respiratory sleep staging. We compared the estimated sleep stages against those derived from PSG and determined performance. Epoch-by-epoch agreement with four-class scoring (Wake, REM, N1+N2, N3) reached a Cohen's kappa coefficient of agreement of 0.68 and an accuracy of 80.8%. For Wake vs. Sleep classification, an accuracy of 93.3% was obtained, with a sensitivity of 78.7% and a specificity of 96.6%. We showed that cardiorespiratory signals obtained from a chest-worn accelerometer can be used to estimate sleep stages among a population that is diverse in age, BMI, and prevalence of sleep disorders. This opens up the path towards various clinical applications in sleep medicine.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24175717