Warby, Simon C.3; Wendt, Sabrina Lyngbye4; Welinder, Peter9; Munk, Emil GS3; Carrillo, Oscar3; Sørensen, Helge Bjarup Dissing6; Jennum, Poul7; Peppard, Paul E.8; Perona, Pietro9; Mignot, Emmanuel3
1 Department of Electrical Engineering, Technical University of Denmark2 Biomedical Engineering, Department of Electrical Engineering, Technical University of Denmark3 Stanford University4 Department of Applied Mathematics and Computer Science, Technical University of Denmark5 California Institute of Technology6 Copenhagen Center for Health Technology, Center, Technical University of Denmark7 Copenhagen University Hospital8 University of Wisconsin-Madison9 California Institute of Technology
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.