Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods
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Sleep-spindle detection : crowdsourcing and evaluating performance of experts, non-experts and automated methods. / Warby, Simon C; Wendt, Sabrina L; Welinder, Peter; Munk, Emil G S; Carrillo, Oscar; Sorensen, Helge B D; Jennum, Poul; Peppard, Paul E; Perona, Pietro; Mignot, Emmanuel.
In: Nature Methods, Vol. 11, No. 4, 04.2014, p. 385-392.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Sleep-spindle detection
T2 - crowdsourcing and evaluating performance of experts, non-experts and automated methods
AU - Warby, Simon C
AU - Wendt, Sabrina L
AU - Welinder, Peter
AU - Munk, Emil G S
AU - Carrillo, Oscar
AU - Sorensen, Helge B D
AU - Jennum, Poul
AU - Peppard, Paul E
AU - Perona, Pietro
AU - Mignot, Emmanuel
PY - 2014/4
Y1 - 2014/4
N2 - 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.
AB - 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.
KW - Aged
KW - Algorithms
KW - Automation
KW - Crowdsourcing
KW - Electroencephalography
KW - Humans
KW - Internet
KW - Middle Aged
KW - Sleep Stages
U2 - 10.1038/nmeth.2855
DO - 10.1038/nmeth.2855
M3 - Journal article
C2 - 24562424
VL - 11
SP - 385
EP - 392
JO - Nature Methods
JF - Nature Methods
SN - 1548-7091
IS - 4
ER -
ID: 137620278