Unobtrusive Monitoring of Sleep Cycles: A Technical Review
Abstract
:1. Introduction
2. Methodology
3. Literature Review
4. Discussion and Viewpoints
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Keywords |
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References
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Study | Objective | Mode of Monitoring | Subjects | Validation Method | Evaluation/Result |
---|---|---|---|---|---|
Nam et al. (2016) [21] | Monitoring sleep quality using vital signs. | Tri-Axial Accelerometer and Pressure Sensor | 10 | Validated with PSG and video camera | ⇨ The results showed that the proposed method can measure vital signs affecting sleep quality. ⇨ The estimators of the sleep quality equation were consistent with reference signals. |
Nguyen et al. (2016) [22] | Presenting a lightweight and inexpensive wearable sensing system. | LIBS | 8 | Validated with PSG | ⇨ The system produced comparable accuracy with PSG for sleep stages classification. |
Gu et al. (2016) [23] | Detecting transition between sleep stages for sleep quality monitoring and intelligent wake-up call. | Mobile service–Sleep Hunter | 15 | Validated with existing actigraphy-based products, Zeo and Jawbone Up | ⇨ Testing the data over one month provided that the detection accuracy of Sleep Hunter was 64.55%. |
Tal et al. (2017) [24] | Validating the efficacy of the system for detecting sleep/wake state and sleep parameters against PSG. Testing if the system can detect sleep architecture in various sleeping conditions. | EarlySense system made up of piezoelectric sensor and a mobile application | 63 | Validated with PSG | ⇨ Relative to PSG, the system showed sleep detection sensitivity, specificity, and accuracy of 92.5%, 80.4%, and 90.5%, respectively. |
Guettari et al. (2017) [25] | Detecting the presence of a person in bed and producing an estimation of the sleep quality. | Thermopile sensor | 13 | Validated with PSG | ⇨ The obtained evaluation results have shown 87% of good classifications with 95% confidence intervals for recognition of the three deducted stages. |
Seba et al. (2017) [26] | Validating the use of a thermal radiation sensor as a sleep analysis sensor. Analyzing physical activity and thermal radiation during sleep. | Thermopile sensor, thermal camera, accelerometer, iButton | 1 | Connected to an acetimeter consisting of an inertial unit fixed on the wrist of the patient | ⇨ The study validated the efficacy of using temperature sensors for the extraction of skin temperature, actimetry, and the presence, absence, and position of a patient in a bed. |
Zambotti et al. (2017) [27] | Comparing the output of a multi-sensor sleep tracker (ŌURA ring) to PSG for measuring sleep and sleep phases. | ŌURA ring | 41 | Validated with PSG | ⇨ The ŌURA ring showed good agreement with the PSG measurements of total sleep time, sleep onset latency, and wake after sleep onset. |
Zambotti et al. (2018) [28] | Comparing the performance of a consumer multi-sensory wristband (Fitbit Charge 2) in measuring sleep stage classification versus PSG. | Fitbit Charge 2 | 44 | Validated with PSG | ⇨ Fitbit achieved 82% accuracy in sleep cycle classification. It overestimated total sleep time and “light sleep” but it underestimated sleep onset latency and “deep sleep”. |
Pallesen et al. (2018) [29] | Validating the impulse radio ultra-wideband pulse-doppler radar technology against PSG for sleep assessment. | Novelda XeThru radar | 12 | Validated with PSG | ⇨ The mean values obtained for accuracy, sensitivity, specificity, and Cohen kappa were 0.931, 0.961, 0.695, and 0.670, respectively. |
Tuominen et al. (2019) [30] | Validating the accuracy of a BCG Beddit Sleep Tracker (BST) for sleep monitoring. | Beddit Sleep Tracker | 10 | Validated through comparison with PSG | ⇨ BST was able to identify sleep onset latency with some accuracy. However, it underestimated wake after sleep onset and overestimated total sleep time and sleep efficiency. ⇨ BST did not distinguish between NREM stages and did not detect the REM stage. |
Kalkbrenner et al. (2019) [31] | Assessing a novel type-4 monitoring system for automated sleep staging. | Type-4 monitoring system | 53 | Validated with PSG | ⇨ The system provided satisfactory results for three-stage sleep classification with an accuracy of 76.3% and Cohen’s kappa of 0.42. |
Lauteslager et al. (2020) [32] | Assessing the performance of a radar-based system for sleep staging performance. | Circadia Contactless Breathing Monitor (model C100) | 9 | Validated with PSG | ⇨ The system produced an overall accuracy of 66.7%. |
Zhang et al. (2021) [33] | Present the model, design, and implementation of SMARS, a sleep monitoring system based on ambient radio signals. | Ambient radio signals | 6 | Validated with PSG and Four state-of-the-art RF-based respiratory monitoring systems | ⇨ Accuracy of 88.4% for three-stage classification, coverage of up 8–10 m, and detection rate of 80%. |
Yu et al. (2021) [15] | Presenting a Wi-Fi-Sleep, a sleep stage monitoring system to monitor and classify sleep. | Wi-Fi transceivers | 12 | Ground truth was obtained from PSG. The performance was Validated with SMARS and RF-Sleep | ⇨ Wi-Fi-Sleep showed 81.8% accuracy for four-stage sleep classification. |
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Siyanbade, J.; Abdulrazak, B.; Sadek, I. Unobtrusive Monitoring of Sleep Cycles: A Technical Review. BioMedInformatics 2022, 2, 204-216. https://doi.org/10.3390/biomedinformatics2010013
Siyanbade J, Abdulrazak B, Sadek I. Unobtrusive Monitoring of Sleep Cycles: A Technical Review. BioMedInformatics. 2022; 2(1):204-216. https://doi.org/10.3390/biomedinformatics2010013
Chicago/Turabian StyleSiyanbade, Juwonlo, Bessam Abdulrazak, and Ibrahim Sadek. 2022. "Unobtrusive Monitoring of Sleep Cycles: A Technical Review" BioMedInformatics 2, no. 1: 204-216. https://doi.org/10.3390/biomedinformatics2010013
APA StyleSiyanbade, J., Abdulrazak, B., & Sadek, I. (2022). Unobtrusive Monitoring of Sleep Cycles: A Technical Review. BioMedInformatics, 2(1), 204-216. https://doi.org/10.3390/biomedinformatics2010013