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2021
DOI: 10.1016/j.isci.2021.102461
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Recent advances in wearable sensors and portable electronics for sleep monitoring

Abstract: Despite the increasing awareness of the importance of sleep, the number of people suffering from insufficient sleep has increased every year. The gold-standard sleep assessment uses polysomnography (PSG) with various sensors to identify sleep patterns and disorders. However, due to the high cost of PSG and limited availability, many people with sleep disorders are left undiagnosed. Recent wearable sensors and electronics enable portable, continuous monitoring of sleep at home, overcoming the limitations of PSG… Show more

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Cited by 114 publications
(67 citation statements)
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References 73 publications
(123 reference statements)
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“…The results of these experiments clearly show that, compared to using just raw signal, converting the signal to multi-taper spectrogram as the input data provides not only comparable or higher classification performance within the public dataset but also superior transferability of the trained model for the classification of another dataset. The average inter-scorer agreement on standard PSG data was usually reported between 82% and 89% [3]. In agreement with this reported value, the average agreement between the two expert scorers of the ISRUC dataset was calculated to be 82.00%, with a Cohen's kappa value of 0.766.…”
Section: Performance Comparison With Other Workmentioning
confidence: 55%
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“…The results of these experiments clearly show that, compared to using just raw signal, converting the signal to multi-taper spectrogram as the input data provides not only comparable or higher classification performance within the public dataset but also superior transferability of the trained model for the classification of another dataset. The average inter-scorer agreement on standard PSG data was usually reported between 82% and 89% [3]. In agreement with this reported value, the average agreement between the two expert scorers of the ISRUC dataset was calculated to be 82.00%, with a Cohen's kappa value of 0.766.…”
Section: Performance Comparison With Other Workmentioning
confidence: 55%
“…To evaluate the performance of the sleep stage classification, there are multiple performance metrics being used in the field, including sensitivity, specificity, and F-measure. Among these metrics, the accuracy rate and Cohen's kappa coefficient are the most commonly used metrics [3], so these metrics are presented and used for comparison in this table. Most of the existing works focused on analyzing public sleep datasets, except for a few cases.…”
Section: Performance Comparison With Other Workmentioning
confidence: 99%
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“…There is a vast body of literature on sleep tracking solutions (surveyed in [41,56,63,67,84]). Our core contribution is the development and evaluation of an unobtrusive all-textile sleep monitoring solution that can capture all signals of interest to sleep.…”
Section: State-of-the-art In Sleep Trackingmentioning
confidence: 99%
“… 25 Earlier consumer wearables had relatively poor and variable performance, but newer devices have shown greater accuracy and consistency of sleep assessment which continue to improve. 2 , 5 , 26 , 27 To guide their own usage of collected data, most users, clinicians, and researchers, are interested in improvements of two major areas, the detection of sleep and wake, from which bedtime, waketime, wake after sleep onset, total sleep time and sleep efficiency are derived, and secondly, the accuracy and consistency of sleep staging, particularly of slow wave sleep and REM. Researchers intending to use consumer wearables are additionally concerned about whether their collected data can benefit from continued refinements to data collection or processing that may help “future-proof” legacy data.…”
Section: Introductionmentioning
confidence: 99%