Polysomnographic pattern recognition for automated classification of sleep-waking states in infants
Open accessAbstract
A robust, automated pattern recognition system for polysomnography data targeted to the sleep-waking state and stage identification is presented. Five patterns were searched for: slow-delta and theta wave predominance in the background electro-encephalogram (EEG) activity; presence of sleep spindles in the EEG; presence of rapid eye movements in an electro-oculogram; and presence of muscle tone in an electromyogram. The performance of the automated system was measured indirectly by evaluating sleep staging, based on the experts' accepted methodology, to relate the detected patterns in infants over four months of post-term age. The set of sleep-waking classes included wakefulness, REM sleep and non-REM sleep stages I, II, and III-IV. Several noise and artifact rejection methods were implemented, including filters, fuzzy quality indices, windows of variable sizes and detectors of limb movements and wakefulness. Eleven polysomnographic recordings of healthy infants were studied. The ages of the subjects ranged from 6 to 13 months old. Six recordings counting 2665 epochs were included in the training set. Results on a test set (2,369 epochs from five recordings) show an overall agreement of 87.7% (kappa 0.840) between the automated system and the human expert. These results show significant improvements compared with previous work.
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The automated system's performance was tested indirectly by analysing different sleep phases in newborns with 70% accuracy.
“The automated system’s performance was tested indirectly by analysing different sleep phases in newborns with 70% accuracy [188].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [188]
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Review of Wearable Devices and Data Collection Considerations for Connected Health
Vijayan, V., Connolly, J. P., Condell, J., McKelvey, N., & Gardiner, P. (2021) · Sensors · doi:10.3390/s21165589 · Cited by 435 · see citing works
- Authors
- Estévez, P. A., Held, C. M., Holzmann, C. A., Perez, C. A., Pérez, J. P., Heiss, J., Garrido, M., & Peirano, P.
- Affiliations
- University of Chile (CL)
- Corresponding author
- Estévez, P. A.
- Venue
- Medical & Biological Engineering & Computing
- Published
- 2002-01
- Volume
- 40
- Issue
- 1
- Pages
- 105-113
- ISSN
- 0140-0118 (ISSN-L)
- 1741-0444
- OpenAlex
- W2032156141
- Cited by
- 69
- References
- 38
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2023: 3 · 2022: 1 · 2021: 6 · 2020: 4 · 2019: 4 · 2018: 4 · 2017: 2 · 2016: 5 · 2015: 8 · 2014: 3 · 2013: 2 · 2012: 5
- FWCI
- 0.67
- Type
- journal article
- Language
- en
- Open access
- green
- americanae.aecid.es
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Medical & Biological Engineering & Computing (publishedVersion)
- PubMed (publishedVersion)
- Americanae (AECID Library) (submittedVersion, other-oa)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, other-oa)
- Funders
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (HD 33487, R01HD033487)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Life Sciences › Neuroscience › Cognitive Neuroscience › Sleep and Wakefulness Research
- Keywords
- polysomnography, sleep-wake state classification, sleep stage classification, infants, automatic pattern recognition, EEG slow waves, sleep spindles, REM sleep, eye movement detection, artifact rejection
- MeSH terms
- Algorithms, Electromyography, Electrooculography, Female, Humans, Infant, Male, Pattern Recognition, Automated, Signal Processing, Computer-Assisted, Sleep Stages, Polysomnography
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