Validation of sleep stage classification using non-contact radar technology and machine learning (Somnofy®)

Abstract

Objective: To validate automatic sleep stage classification using deep neural networks on sleep assessed by radar technology in the commercially available sleep assistant Somnofy® against polysomnography (PSG). Methods: Seventy-one nights of overnight sleep in healthy individuals were assessed by both PSG and Somnofy at two different institutions. The Somnofy unit was placed in two different locations per room (nightstand and wall). The sleep algorithm was validated against PSG using a 25-fold cross validation technique, and performance was compared to the inter-rater reliability between the PSG sleep scored by two independent sleep specialists. Results: Epoch-by-epoch analyses showed a sensitivity (accuracy to detect sleep) and specificity (accuracy to detect wake) for Somnofy of 0.97 and 0.72 respectively, compared to 0.99 and 0.85 for the PSG scorers. The sleep stage differentiation for Somnofy was 0.75 for N1/N2, 0.74 for N3 and 0.78 for R, whilst PSG scorers ranged between 0.83 and 0.96. The intraclass correlation coefficient revealed excellent and good reliability for total sleep time and sleep efficiency, while sleep onset and R latency had poor agreement. Somnofy underestimated total wake time by 5 min and N1/N2 by 3 min. N3 was overestimated by 4 min and R by 3 min. Results were independent of institution and sensor location. Conclusion: Somnofy showed a high accuracy staging sleep in healthy individuals and has potential to assess sleep quality and quantity in a sample of healthy, mostly young adults. More research is needed to examine performance in children, older individuals and those with sleep disorders.

Reciprocal Associations Between Sleep, Mental Strain, and Training Load in Junior Endurance Athletes and the Role of Poor Subjective Sleep Quality

“…Therefore, the statistical analyses exploring whether the reciprocal associations between sleep, mental strain, and training load are moderated by sleeper category (good vs. poor) should be interpreted with caution. Last, although the device used for sleep monitoring used in the current study performed well in the validation study against PSG, the relationship of the Somnofy-derived sleep parameters and PSG data is not perfect ( Toften et al, 2020 ). Hence, some measurement error regarding the sleep variables should be acknowledged.…”

“…Still, Somnofy seems to provide more accurate sleep staging than several comparable non-obtrusive sleep assessment alternatives ( Peake et al, 2018 ). For a full technical overview of the sleep monitor, including its limitations and results of its validation, see Toften et al, 2020 .…”

“…As a solution to the methodological limitations in previous research, the present study performed objective, repeated measurements of sleep by employing novel technology in junior endurance athletes. The novel sleep monitoring device is based on an impulse radio ultra-wideband (IR-UWB) radar sensor technique and machine learning and has been validated against PSG with substantial agreement on both sleep/wake and sleep staging ( Toften et al, 2020 ). The primary aim of the present study is to investigate the reciprocal and temporal associations between sleep, mental strain, and training load.…”

Let there be blue-depleted light: in-patient dark therapy, circadian rhythms and length of stay

“…The system can detect movements of less than 1 mm and can be used with very high sensitivity and specificity for detecting sleep-wake state compared with polysomnography (Toften 2020). It avoids the use of any other remote approaches, such as visual observation by cameras, and is well-tolerated among patients.…”

Associations Between Sleep Patterns and Performance Development Among Norwegian Chess Players

“…Recently, a full validation of Somnofy against the gold standard in sleep measurement -manually scored polysomnography (PSG) was carried out. The study has shown Somnofy to be an adequate measure of sleep and wake, as well as sleep stages, in a healthy adult population (Toften et al, 2020). For the purposes of this study, the following sleep variables were obtained from the Somnofy sleep monitor: sleep onset, sleep offset, time in bed, sleep onset latency, total sleep time, time and percentage in sleep stages (light, deep and REM), sleep efficiency, and respiration rate (RP).…”