Journal
NATURE METHODS
Volume 11, Issue 4, Pages 385-+Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/NMETH.2855
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Funding
- Brain and Behavior Research Foundation
- Canadian Institutes of Health Research
- US National Institutes of Health (NIH) [NS23724]
- NAVY [ONR-MURI N00014-06-1-0734, UCLA-MURI N00014-10-1-0933]
- National Heart, Lung, and Blood Institute [R01HL62252]
- National Center for Research Resources at the NIH [1UL1RR025011]
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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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