Sleep and wake classification with ECG and respiratory effort signals
Open accessAbstract
We describe a method for the online classification of sleep/wake states based on cardiorespiratory signals produced by wearable sensors. The method was conceived in view of its applicability to a wearable sleepiness monitoring device. The method uses a fast Fourier transform as the main feature extraction tool and a feedforward artificial neural network as a classifier. We show that when the method is applied to data collected from a single young male adult, the system can correctly classify, on average, 95.4% of unseen data from the same user. When the method is applied to classify data from multiple users with the same age and gender, its accuracy is reduced to 85.3%. However, receiver operating characteristic analysis shows that compared to actigraphy, the proposed method produces a more balanced correct classification of sleep and wake periods. Additionally, by adjusting the classification threshold of the neural classifier, 86.7% of correct classification is obtained.
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Other tools for feature selection used in literature include threshold-based rules, ANOVA, and Fourier transforms.
“Other tools for feature selection used in the literature includes threshold-based rules [61], analysis of variance (ANOVA) [51], and Fourier transforms [52].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [52]
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Banaee, H., Ahmed, M., & Loutfi, A. (2013) · Sensors · doi:10.3390/s131217472 · Cited by 452 · see citing works
- Authors
- Karlen, W., Mattiussi, C., & Floreano, D.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Venue
- IEEE Transactions on Biomedical Circuits and Systems
- Published
- 2009-04
- Volume
- 3
- Issue
- 2
- Pages
- 71-78
- ISSN
- 1932-4545 (ISSN-L)
- 1940-9990
- OpenAlex
- W2098422376
- Cited by
- 134
- References
- 26
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 6 · 2023: 5 · 2022: 5 · 2021: 15 · 2020: 10 · 2019: 7 · 2018: 7 · 2017: 14 · 2016: 8 · 2015: 11 · 2014: 7 · 2013: 13 · 2012: 4
- FWCI
- 4.41
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Locations
- IEEE Transactions on Biomedical Circuits and Systems (publishedVersion)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- 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-wake classification, electrocardiogram, respiratory effort, wearable sensors, cardiorespiratory signals, fast Fourier transform features, feedforward neural networks
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