The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods
Open accessAbstract
Sleep spindles are discrete, intermittent patterns of brain activity observed in human electroencephalographic data. Increasingly, these oscillations are of biological and clinical interest because of their role in development, learning and neurological disorders. We used an Internet interface to crowdsource spindle identification by human experts and non-experts, and we compared their performance with that of automated detection algorithms in data from middle- to older-aged subjects from the general population. We also refined methods for forming group consensus and evaluating the performance of event detectors in physiological data such as electroencephalographic recordings from polysomnography. Compared to the expert group consensus gold standard, the highest performance was by individual experts and the non-expert group consensus, followed by automated spindle detectors. This analysis showed that crowdsourcing the scoring of sleep data is an efficient method to collect large data sets, even for difficult tasks such as spindle identification. Further refinements to spindle detection algorithms are needed for middle- to older-aged subjects.
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Retracted
Metadata
- Authors
- 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.
- Venue
- Nature Methods
- Published
- 2014-02-23
- Volume
- 11
- Issue
- 4
- Pages
- 385-392
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2083355945
- Cited by
- 383
- References
- 10
- FWCI
- 14.98
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
