Sleep stage classification based on respiratory signal

Tataraidze, Alexander; Anishchenko, Lesya; Korostovtseva, Lyudmila; Kooij, Bert Jan; Bochkarev, Mikhail; Sviryaev, Yurii

doi:10.1109/embc.2015.7318373

Cited by 22 publications

(16 citation statements)

References 13 publications

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“…Respiration monitoring during sleep is particularly useful. For example, the respiration signal can be used to infer the person's sleep stages (light, deep or REM sleep) [47,21,39] and diagnose sleep disorders [4,12].…”

Section: Introductionmentioning

confidence: 99%

Extracting Multi-Person Respiration from Entangled RF Signals

Yue

Wang

et al. 2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

148

104

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Recent advances in wireless systems have demonstrated the possibility of tracking a person's respiration using the RF signals that bounce off her body. The resulting breathing signal can be used to infer the person's sleep quality and stages; it also allows for monitoring sleep apnea and other sleep disordered breathing; all without any body contact. Unfortunately however past work fails when people are close to each other, e.g., a couple sharing the same bed. In this case, the breathing signals of nearby individuals interfere with each other and super-impose in the received signal.This thesis presents DeepSleep, the first RF-based respiration monitoring system that can recover the breathing signals of multiple individuals even when they are separated by zero distance. To design DeepSleep, we model interference due to multiple reflected RF signals and demonstrate that the original breathing can be recovered via independent component analysis. We design a full system that eliminates interference and recovers the original breathing signals. We empirically evaluate DeepSleep using 21 nights of sleep and over 150 hours of data from 13 couples who share the bed. Our results show that DeepSleep is very accurate. Specifically, the differences between the breathing signals it recovers and the ground truth are on par with the difference between the same breathing signal measured at the person's chest and belly. Then, I would like to thank my collaborator Hao He. Hao and I have an enjoyable collaboration during this study. We have discussed every corner of our system and spent countless hours together improving its performance.

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Section: Introductionmentioning

confidence: 99%

Extracting Multi-Person Respiration from Entangled RF Signals

Yue

Wang

et al. 2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

148

104

View full text Add to dashboard Cite

show abstract

“…The accuracy of RM‐OSM ranges from 76.2% to 80.38%, 227,235–239 with an average accuracy of 79.29%. Moreover, as the specific indicator of RM output, AHI has an accuracy of 93%.…”

Section: Comparison Among Assessment Methodsmentioning

confidence: 99%

Objective sleep assessments for healthy people in environmental research: A literature review

Lian

2022

Indoor Air

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To date, although many studies had focused on the impact of environmental factors on sleep, how to choose the proper assessment method for objective sleep quality was often ignored, especially for healthy subjects in bedroom environment. In order to provide methodological guidance for future research, this paper reviewed the assessments of objective sleep quality applied in environmental researches, compared them from the perspective of accuracy and interference, and statistically analyzed the impact of experimental type and subjects' information on method selection. The review results showed that, in contrast to polysomnography (PSG), the accuracy of actigraphy (ACT), respiratory monitoring‐oxygen saturation monitoring (RM‐OSM), and electrocardiograph (ECG) could reach up to 97%, 80.38%, and 79.95%, respectively. In terms of sleep staging, PSG and ECG performed the best, ACT the second, and RM‐OSM the worst; as compared to single methods, mix methods were more accurate and better at sleep staging. PSG interfered with sleep a great deal, while ECG and ACT could be non‐contact, and thus, the least interference with sleep was present. The type of experiment significantly influenced the choice of assessment method (p < 0.001), 85.3% of researchers chose PSG in laboratory study while 82.5% ACT in field study; moreover, PSG was often used in a relatively small number of young subjects, while ACT had a wide applicable population. In general, researchers need to pay more attention at selection of assessments in future studies, and this review can be used as a reliable reference for experimental design.

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“…It was found that the deep learning models performed extremely well in comparison to the probabilistic models. Tataraidze et al (2015), performed a classification task by fetching the data from polysomnography study and classify the sleep, awake, REM, and non-REM. The primary aspect of the work is the addition of some different heuristics, which can increase the performance of the model.…”

Section: Related Workmentioning

confidence: 99%

A Novel Sleep Scoring Algorithm-Based Framework and Sleep Pattern Analysis Using Machine Learning Techniques

Chakraborty

Aich

Kim

2021

International Journal of System Dynamics Applications

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Maintaining the suited amount of sleep is considered the prime component for maintaining a proper and adequate health condition. Often it has been observed that people having sleep inconsistency tend to jeopardize the health and appeal to many physiological and psychological disorders. To overcome such difficulties, it is often required to keep a requisite note of the duration and quality of sleep that one is having. This work defines an algorithm that can be utilized in smart wearables or mobile phones to perceive the duration of sleep and also to classify a particular instance as slept or awake on the basis of data fetched from the triaxial accelerometer. A comparative analysis was performed based on the results obtained from some previously developed algorithms, rule-based models, and machine learning models, and it was observed that the algorithm developed in the work outperformed the previously developed algorithms. Moreover, the algorithm developed in the work will very much define the scoring of sleep of an individual for maintaining a proper health balance.

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Sleep stage classification based on respiratory signal

Cited by 22 publications

References 13 publications

Extracting Multi-Person Respiration from Entangled RF Signals

Extracting Multi-Person Respiration from Entangled RF Signals

Objective sleep assessments for healthy people in environmental research: A literature review

A Novel Sleep Scoring Algorithm-Based Framework and Sleep Pattern Analysis Using Machine Learning Techniques

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