Processing of signals recorded through smart devices: Sleep-quality assessment
Abstract
In this paper, we discuss the possibility of performing a sleep evaluation from signals, which are not usually used for this purpose. In particular, we take into consideration the heart rate variability (HRV) and respiratory signals for automatic sleep staging, arousals detection, and apnea recognition. This is particularly useful for wearable or textile devices that could be employed for home monitoring of sleep. The HRV and the respiration were analyzed in the frequency domain, and the statistics on the spectral and cross-spectral parameters put into evidence the possibility of a sleep evaluation on their basis. Comparison with traditional polysomnography (PSG) revealed a classification accuracy of 89.9% in rapid eye movement (REM) non-REM sleep separation and an accuracy of 88% for sleep apnea detection. Additional information can be achieved from the number of microarousals recognized in correspondence of typical modifications in the HRV signal. The obtained results support the idea of automatic sleep evaluation and monitoring through signals that are not traditionally used in clinical PSG, but can be easily recorded at home through wearable devices (for example, a sensorized T-shirt) or systems integrated into the environment (a sensorized bed). This is a first step for the development of systems for sleep screening on large populations that can constitute a complement for the traditional clinical evaluation.
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Recent works in diagnosis/decision making involve estimating severity of health episodes of patients with chronic disease, sleep issues such as polysomnography and apnea, estimation and classification of health conditions, and emotion recognition.
“Some examples of recent works in this area involve estimating the severity of health episodes of patients suffering chronic disease [44–46], sleep issues such as polysomnography and apnea [47,48], estimation and classification of health conditions [49,50], and emotion recognition [51].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [47]
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- Authors
- Bianchi, A. M., Mendez, M. O., & Cerutti, S.
- Affiliations
- Politecnico di Milano (IT)
- Autonomous University of San Luis Potosí (MX)
- Venue
- IEEE Transactions on Information Technology in Biomedicine
- Published
- 2010-05
- Volume
- 14
- Issue
- 3
- Pages
- 741-747
- ISSN
- 1089-7771 (ISSN-L)
- OpenAlex
- W2108346142
- Cited by
- 61
- References
- 37
- Citations by year
- 2026: 3 · 2024: 3 · 2023: 1 · 2022: 2 · 2021: 6 · 2020: 8 · 2019: 5 · 2018: 3 · 2017: 2 · 2016: 2 · 2015: 6 · 2014: 5 · 2013: 8 · 2012: 7
- FWCI
- 2.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Information Technology in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- Virtual Community of Pathological Anatomy (University of Castilla La Mancha) (submittedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Keywords
- sleep quality assessment, automatic sleep staging, heart rate variability, respiratory signals, sleep apnea detection, microarousals, wearable devices, textile sensors, home sleep monitoring, cross-spectral analysis, polysomnography comparison
- MeSH terms
- Clothing, Heart Rate, Humans, Pattern Recognition, Automated, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Sleep Stages, Polysomnography, Computers, Handheld
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