Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods


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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Figure 1: Generation of the gold standard and spindle-detection performance of individual experts.
Figure 2: By-event and by-subject characteristics of 1,987 spindles in the gold-standard data set.
Figure 3: Consensus and performance of the non-expert group for spindle detection.
Figure 4: Automated spindle-detector performance.
Figure 5: Performance of experts, non-experts and automated spindle detection algorithms.


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The authors thank the Registered Polysomnographic Technologist (RPSGT) experts who participated in the spindle identification task as well as the participants and organizers of the Wisconsin Sleep Cohort who provided the polysomnography data. We also thank C. Liang, E. Leary, H. Ollila and H. Kraemer for their helpful discussions and E.Þ. Ágústsson and H. Moore for their input in the pilot study for this project. We are grateful to the authors of the previously published algorithms who generously shared their code and knowledge about spindle detectors. S.C.W. is supported by the Brain and Behavior Research Foundation and as a Canadian Institutes of Health Research Banting Fellow. E.M. is supported by US National Institutes of Health (NIH) grant NS23724. P.P. received funding from the Caltech SURF program and the NAVY (ONR-MURI N00014-06-1-0734 and UCLA-MURI N00014-10-1-0933). EEG data collection was supported by grants from the National Heart, Lung, and Blood Institute (grant R01HL62252) and the National Center for Research Resources (grant 1UL1RR025011) at the NIH.

Author information




S.C.W., E.M. and P.P. designed the research. P.W. and P.P. designed and coded the Internet interface. S.C.W. and P.W. collected the spindle scoring data. S.C.W. and S.L.W. performed the data analysis. S.L.W. wrote the code to implement the automated spindle detectors. All authors provided input on data analysis and interpretation. P.E.P. also provided source EEG data. H.B.D.S., P.J., E.M. and P.P. also provided financial support. S.C.W., S.L.W. and E.M. wrote the manuscript, which was discussed and edited by all authors.

Corresponding authors

Correspondence to Pietro Perona or Emmanuel Mignot.

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The authors declare no competing financial interests.

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Supplementary Figures 1–13, Supplementary Tables 1–8 and Supplementary Note (PDF 2846 kb)

Supplementary Software

MATLAB code (text-based .m files) for automatic spindle detectors a1-a6. (ZIP 17 kb)

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Warby, S., Wendt, S., Welinder, P. et al. Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods. Nat Methods 11, 385–392 (2014).

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