0275 Deep Learning-based Sleep Detection using Torso Patch Vital Signs Improves Sleep-Wake Detection over Wrist Actigraphy

• Published in: Sleep (New York, N.Y.) Vol. 46; no. Supplement_1; p. A122
• Main Authors: Pipke, Matt, Alla, Srilakshmi, Sekaric, Jadranka, Richards, Dylan, Gendy, Maged, Abbott, Sabra, Grimaldi, Daniela, Reid, Kathryn, Zee, Phyllis
• Format: Journal Article
• Language: English
• Published: Westchester Oxford University Press 29.05.2023
• Subjects: Algorithms; Deep learning; Neural networks; Sensors; Sleep
• ISSN: 0161-8105; 1550-9109; 1550-9109
• DOI: 10.1093/sleep/zsad077.0275

Introduction Motion-based wearable sensors, typically on wrist, have long been used for free-living sleep detection and quantification. However, it is hard to differentiate sleep from sedentary awake time by immobility alone. Vital signs, like heart rate and respiration rate, can greatly enhance determination of wake-sleep state, and are easily monitored with newer wearable sensors. Deep learning techniques are particularly adept at learning labeled physiological states. By combining movement plus vital signs in a deep neural network algorithm, improved sleep detection, fragmentation and sleep staging should be possible compared to activity alone. We report on performance of a deep learning sleep detection and REM/NREM algorithm providing 24-hour evaluation with high specificity using data from a torso-wearable patch sensor as compared to polysomnography (PSG). Methods Twenty-six healthy adults (mean age 53.7 years, 81% female) contributed 150 nights of PSG during laboratory visits, during which participants simultaneously wore a multi-day skin-adherent patch with continuous single-lead ECG and 3-axis accelerometer streams, as well as a wrist activity monitor. A pre-trained deep neural network algorithm generated epoch-level Wake/REM/NREM classification (Sleep equals REM plus NREM) using vital signs and movement derived from the patch sensor ECG and accelerometer waveforms and was then compared to expert human staging of PSGs. The wrist actigraphy sleep-wake determinations (Actiware) were also compared to PSG. Results Data includes 900 hours sleeping and 139 hours awake, of which 195 hours of sleep were in REM state. Using patch data, the deep neural net algorithm achieved 92% sensitivity and 85% specificity to detect sleep as compared to PSG; REM was detected with 85% sensitivity and 97% specificity. By comparison, the wrist motion-based algorithm only exhibited 33% specificity and 95% sensitivity, essentially overcalling immobile wake as sleep. Conclusion Sleep evaluation in free-living environments with wearable sensors can be greatly improved over conventional motion-based wrist sensors by leveraging continuous vital signs. Deep learning-trained neural network algorithms are particularly effective for use with such data, as demonstrated with this algorithm. Support (if any) R01 HL140580 and P01 AG011412