Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Warby SC

PROVIDER: S-EPMC3972193 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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

Warby Simon C SC Wendt Sabrina L SL Welinder Peter P Munk Emil G S EG Carrillo Oscar O Sorensen Helge B D HB Jennum Poul P Peppard Paul E PE Perona Pietro P Mignot Emmanuel E

Nature methods 20140223 4

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 ge ...[more]

PMID: 24562424

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Dataset Information

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

Publications

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

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