The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Computer-assisted sleep staging
Abstract
To address the subjectivity in manual scoring of polysomnograms, a computer-assisted sleep staging method is presented in this paper. The method uses the principles of segmentation and self-organization (clustering) based on primitive sleep-related features to find the pseudonatural stages present in the record. Sample epochs of these natural stages are presented to the user, who can classify them according to the Rechtschaffen and Kales (RK) or any other standard. The method then learns from these samples to complete the classification. This step allows the active participation of the operator in order to customize the staging to his/her preferences. The method was developed and tested using 12 records of varying types (normal, abnormal, male, female, varying age groups). Results showed an overall concurrence of 80.6% with manual scoring of 20-s epochs according to RK standard. The greatest amount of errors occurred in the identification of the highly transitional Stage 1, 54% of which was misclassified into neighboring stages 2 or Wake.
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Cites
Dement, W., & Kleitman, N. (1957) · Journal of Experimental Psychology · doi:10.1037/h0048189 · Cited by 971
Polysomnographic pattern recognition for automated classification of sleep-waking states in infants
Estévez, P. A., Held, C. M., Holzmann, C. A., Perez, C. A., Pérez, J. P., Heiss, J., Garrido, M., & Peirano, P. (2002) · Medical & Biological Engineering & Computing · doi:10.1007/bf02347703 · Cited by 69
Cited by
Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation
Aboalayon, K., Faezipour, M., Almuhammadi, W., & Moslehpour, S. (2016) · Entropy · doi:10.3390/e18090272 · Cited by 382
Lajnef, T., Chaibi, S., Ruby, P., Aguera, P.-E., Eichenlaub, J.-B., Samet, M., Kachouri, A., & Jerbi, K. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.01.022 · Cited by 318
A comparative review on sleep stage classification methods in patients and healthy individuals
Boostani, R., Karimzadeh, F., & Nami, M. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.12.004 · Cited by 316
An ensemble system for automatic sleep stage classification using single channel EEG signal
Koley, B., & Dey, D. (2012) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2012.09.012 · Cited by 291
Automatic Analysis of Single-Channel Sleep EEG: Validation in Healthy Individuals
Berthomier, C., Drouot, X., Herman-Stoïca, M., Berthomier, P., Prado, J., Bokar-Thire, D., Benoit, O., Mattout, J., & d'Ortho, M.-P. (2007) · Sleep · doi:10.1093/sleep/30.11.1587 · Cited by 294
Metadata
- Authors
- Agarwal, R., & Gotman, J.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2001
- Volume
- 48
- Issue
- 12
- Pages
- 1412-1423
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2036783526
- Cited by
- 219
- References
- 2
- FWCI
- 1.17
- Type
- journal article
- Language
- en
- Open access
- closed
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- Agarwal, R., & Gotman, J. (2001). Computer-assisted sleep staging. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/10.966600
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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