The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Sleep and Wake Classification Based on Heart Rate and Respiration Rate
Open accessAbstract
Sleep plays a significant role in human health. Along with the emergency of diseases related to sleep, sleep monitoring is becoming a hotspot. Traditionally, Polysomnography (PSG) is considered as the gold standard for sleep monitoring, but it is expensive, time-consuming, and uncomfortable, especially not ideal for long-term sleep studies. Thus, exploring alternative sleep monitoring method has drawn much attention of researchers. Currently most of the sleep monitoring methods require complicated signals and feature extraction process. However, this paper innovatively proposes a method using simple signal of heart rate and respiration rate, and integrating the prior knowledge of experts to simplify the feature extraction process, which can make the subsequent classifier distinguish wake state and sleep state easily. In this paper, we used multi-layer neural network as a classifier, getting a Cohen's kappa value of 0.739, and the overall accuracy of classifying sleep and wake state reached to 88%. Furthermore, we improved the ability of detecting wake states greatly by oversampling method, and the sensitivity and specificity reached to 91.3% and 82.3% respectively. Nowadays, the development of various bed sensors makes it easy to obtain the heart rate and respiration rate signals of human beings. Therefore, the method proposed in this paper is conducive to people's long-term sleep monitoring more conveniently and has great significance to people's health.
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“When the sow reclines, it often alternates between being awake and asleep, which affects the sow’s breathing status [52].”
From Modeling of Heat Stress in Sows—Part 1: Establishment of the Prediction Model for the Equivalent Temperature Index of the Sows (2021) · cites this paper as [52]
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Selective scalable secret image sharing with verification
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Sleep stages classification based on heart rate variability and random forest
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Self-citationda Silveira, T. L. T., Kozakevicius, A. D. J., & Rodrigues, C. R. (2016) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-016-1519-4 · Cited by 126
Sleep and Wake Classification With Actigraphy and Respiratory Effort Using Dynamic Warping
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Noncontact Pressure-Based Sleep/Wake Discrimination
Walsh, L. C., McLoone, S., Ronda, J. M., Duffy, J. F., & Czeisler, C. A. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2621066 · Cited by 24
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The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey
Bian, S., Liu, M., Zhou, B., & Lukowicz, P. (2022) · Sensors · doi:10.3390/s22124596 · Cited by 70
Efficient embedded sleep wake classification for open-source actigraphy
Banfi, T., Valigi, N., Galante, M. D., D’Ascanio, P., Ciuti, G., & Faraguna, U. (2021) · Scientific Reports · doi:10.1038/s41598-020-79294-y · Cited by 36
Multi-stage sleep classification using photoplethysmographic sensor
Motin, M. A., Karmakar, C. K., Palaniswami, M. S., Penzel, T., & Kumar, D. (2023) · Royal Society Open Science · doi:10.1098/rsos.221517 · Cited by 27
Cao, M., Zong, C., Wang, X., Teng, G., Zhuang, Y., & Lei, K. (2021) · Animals · doi:10.3390/ani11051472 · Cited by 19
“The respiration rate is also affected by the state of sleep [52].”
BackgroundObjective sleep assessments for healthy people in environmental research: A literature review
Xu, X., & Lian, Z. (2022) · Indoor Air · doi:10.1111/ina.13034 · Cited by 20
Metadata
- Authors
- Li, W., Yang, X. D., Dai, A. N., & Chen, K.
- Affiliations
- Tsinghua University (CN)
- Corresponding author
- Li, W
- Yang, X D
- Dai, A N
- Chen, K
- Venue
- IOP Conference Series: Materials Science and Engineering
- Publisher
- IOP Publishing
- Published
- 2018-10-01
- Volume
- 428
- Pages
- 012017
- ISSN
- 1757-8981 (ISSN-L)
- 1757-899X
- OpenAlex
- W2895675313
- Cited by
- 12
- References
- 8
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 2 · 2022: 3 · 2021: 2 · 2019: 2
- FWCI
- 2.19
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- IOP Conference Series Materials Science and Engineering (publishedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Keywords
- sleep state classification, heart rate, respiration rate, sleep monitoring, wearable sensors, expert knowledge, feature extraction, oversampling, multilayer neural networks, long-term sleep monitoring
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