The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Montreal Archive of Sleep Studies: an open‐access resource for instrument benchmarking and exploratory research
Abstract
Summary: Manual processing of sleep recordings is extremely time‐consuming. Efforts to automate this process have shown promising results, but automatic systems are generally evaluated on private databases, not allowing accurate cross‐validation with other systems. In lacking a common benchmark, the relative performances of different systems are not compared easily and advances are compromised. To address this fundamental methodological impediment to sleep study, we propose an open‐access database of polysomnographic biosignals. To build this database, whole‐night recordings from 200 participants [97 males (aged 42.9 ± 19.8 years) and 103 females (aged 38.3 ± 18.9 years); age range: 18–76 years] were pooled from eight different research protocols performed in three different hospital‐based sleep laboratories. All recordings feature a sampling frequency of 256 Hz and an electroencephalography (EEG) montage of 4–20 channels plus standard electro‐oculography (EOG), electromyography (EMG), electrocardiography (ECG) and respiratory signals. Access to the database can be obtained through the Montreal Archive of Sleep Studies (MASS) website (http://www.ceams-carsm.ca/en/MASS), and requires only affiliation with a research institution and prior approval by the applicant's local ethical review board. Providing the research community with access to this free and open sleep database is expected to facilitate the development and cross‐validation of sleep analysis automation systems. It is also expected that such a shared resource will be a catalyst for cross‐centre collaborations on difficult topics such as improving inter‐rater agreement on sleep stage scoring.
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Rechtschaffen, A. (1968) · Medical Entomology and Zoology · Cited by 11,772
(2007) · Cited by 6,530
The Sleep Heart Health Study: Design, Rationale, and Methods
(1997) · Sleep · doi:10.1093/sleep/20.12.1077 · Cited by 1,331
Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG
Kemp, B., Zwinderman, A. H., Tuk, B., Kamphuisen, H. A. C., & Oberye, J. J. L. (2000) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.867928 · Cited by 953
The Effects of Coffee and Napping on Nighttime Highway Driving: A Randomized Trial
Philip, P., Taillard, J., Moore, N., Delord, S., Valtat, C., Sagaspe, P., & Bioulac, B. (2006) · Annals of Internal Medicine · doi:10.7326/0003-4819-144-11-200606060-00004 · Cited by 650
The case for open computer programs
Ince, D. C., Hatton, L., & Graham-Cumming, J. (2012) · Nature · doi:10.1038/nature10836 · Cited by 535
Warby, S. C., Wendt, S. L., Welinder, P., Munk, E. G. S., Carrillo, O., Sorensen, H. B. D., Jennum, P., Peppard, P. E., Perona, P., & Mignot, E. (2014) · Nature Methods · doi:10.1038/nmeth.2855 · Cited by 383
Mild cognitive impairment in rapid eye movement sleep behavior disorder and Parkinson's disease
Gagnon, J., Vendette, M., Postuma, R. B., Desjardins, C., Massicotte‐Marquez, J., Panisset, M., & Montplaisir, J. (2009) · Annals of Neurology · doi:10.1002/ana.21680 · Cited by 325
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
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
Cited by
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Deep learning-based electroencephalography analysis: a systematic review
Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T. H., & Faubert, J. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab260c · Cited by 1,532
DeepSleepNet: a model for automatic sleep stage scoring based on raw single-channel EEG
Supratak, A., Dong, H., Wu, C., & Guo, Y. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2721116 · Cited by 1,376
Phan, H., Andreotti, F., Cooray, N., Chen, O. Y., & De Vos, M. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2896659 · Cited by 623
ISRUC-Sleep: A comprehensive public dataset for sleep researchers
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Metadata
- Authors
- O'Reilly, C., Gosselin, N., Carrier, J., & Nielsen, T.
- Venue
- Journal of Sleep Research
- Publisher
- Wiley
- Published
- 2014-06-09
- Volume
- 23
- Issue
- 6
- Pages
- 628-635
- ISSN
- 0962-1105 (ISSN-L)
- 1365-2869
- OpenAlex
- W1983256092
- Cited by
- 356
- References
- 37
- FWCI
- 4.60
- Type
- journal article
- Language
- en
- Open access
- closed
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- O'Reilly, C., Gosselin, N., Carrier, J., & Nielsen, T. (2014). Montreal Archive of Sleep Studies: an open‐access resource for instrument benchmarking and exploratory research. Journal of Sleep Research. https://doi.org/10.1111/jsr.12169
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