Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods
A comparative analysis of methods for scoring human sleep data, in particular sleep spindles, from encephalographic recordings is reported. The authors develop methods for crowdsourcing the identification of sleep spindles and compare the detection performance of experts, non-experts and automated a...
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| Published in | Nature methods Vol. 11; no. 4; pp. 385 - 392 |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Nature Publishing Group US
01.04.2014
Nature Publishing Group |
| Subjects | |
| Online Access | Get full text |
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| Abstract | A comparative analysis of methods for scoring human sleep data, in particular sleep spindles, from encephalographic recordings is reported. The authors develop methods for crowdsourcing the identification of sleep spindles and compare the detection performance of experts, non-experts and automated algorithms.
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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| AbstractList | Sleep spindles are discrete, intermittent patterns of brain activity that arise as a result of interactions of several circuits in the brain. 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 from human experts and non-experts, and compared performance with 6 automated detection algorithms in middle-to-older aged subjects from the general population. We also developed a method for forming group consensus, and refined methods of evaluating the performance of event detectors in physiological data such as polysomnography. Compared to the gold standard, the highest performance was by individual experts and the non-expert group consensus, followed by automated spindle detectors. Crowdsourcing the scoring of sleep data is an efficient method to collect large datasets, even for difficult tasks such as spindle identification. Further refinements to automated sleep spindle algorithms are needed for middle-to-older aged subjects. A comparative analysis of methods for scoring human sleep data, in particular sleep spindles, from encephalographic recordings is reported. The authors develop methods for crowdsourcing the identification of sleep spindles and compare the detection performance of experts, non-experts and automated algorithms. 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. 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. 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.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. |
| Audience | Academic |
| Author | Warby, Simon C Wendt, Sabrina L Jennum, Poul Perona, Pietro Sorensen, Helge B D Munk, Emil G S Carrillo, Oscar Mignot, Emmanuel Welinder, Peter Peppard, Paul E |
| AuthorAffiliation | 3 Computational Vision Laboratory, California Institute of Technology, Pasadena, California, USA 2 Danish Center for Sleep Medicine, Glostrup University Hospital, Glostrup, Denmark 5 Department of Population Health Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA 4 Dept. of Electrical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark 1 Center for Sleep Science and Medicine, Stanford University, California, USA |
| AuthorAffiliation_xml | – name: 4 Dept. of Electrical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark – name: 2 Danish Center for Sleep Medicine, Glostrup University Hospital, Glostrup, Denmark – name: 5 Department of Population Health Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA – name: 1 Center for Sleep Science and Medicine, Stanford University, California, USA – name: 3 Computational Vision Laboratory, California Institute of Technology, Pasadena, California, USA |
| Author_xml | – sequence: 1 givenname: Simon C surname: Warby fullname: Warby, Simon C organization: Center for Sleep Science and Medicine, Stanford University – sequence: 2 givenname: Sabrina L surname: Wendt fullname: Wendt, Sabrina L organization: Center for Sleep Science and Medicine, Stanford University, Danish Center for Sleep Medicine, Glostrup University Hospital – sequence: 3 givenname: Peter surname: Welinder fullname: Welinder, Peter organization: Computational Vision Laboratory, California Institute of Technology – sequence: 4 givenname: Emil G S surname: Munk fullname: Munk, Emil G S organization: Center for Sleep Science and Medicine, Stanford University, Danish Center for Sleep Medicine, Glostrup University Hospital – sequence: 5 givenname: Oscar surname: Carrillo fullname: Carrillo, Oscar organization: Center for Sleep Science and Medicine, Stanford University – sequence: 6 givenname: Helge B D surname: Sorensen fullname: Sorensen, Helge B D organization: Department of Electrical Engineering, Technical University of Denmark – sequence: 7 givenname: Poul surname: Jennum fullname: Jennum, Poul organization: Danish Center for Sleep Medicine, Glostrup University Hospital – sequence: 8 givenname: Paul E surname: Peppard fullname: Peppard, Paul E organization: Department of Population Health Sciences, University of Wisconsin–Madison – sequence: 9 givenname: Pietro surname: Perona fullname: Perona, Pietro email: perona@caltech.edu organization: Computational Vision Laboratory, California Institute of Technology – sequence: 10 givenname: Emmanuel surname: Mignot fullname: Mignot, Emmanuel email: mignot@stanford.edu organization: Center for Sleep Science and Medicine, Stanford University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24562424$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Copyright | Springer Nature America, Inc. 2014 COPYRIGHT 2014 Nature Publishing Group Copyright Nature Publishing Group Apr 2014 |
| Copyright_xml | – notice: Springer Nature America, Inc. 2014 – notice: COPYRIGHT 2014 Nature Publishing Group – notice: Copyright Nature Publishing Group Apr 2014 |
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| DOI | 10.1038/nmeth.2855 |
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| References_xml | – reference: De GennaroLFerraraMVecchioFCurcioGBertiniMAn electroencephalographic fingerprint of human sleepNeuroimage20052611412210.1016/j.neuroimage.2005.01.020 – reference: DiekelmannSBornJThe memory function of sleepNat. Rev. Neurosci.2010111141261:CAS:528:DC%2BC3cXktFKi20046194 – reference: AyoubADifferential effects on fast and slow spindle activity, and the sleep slow oscillation in humans with carbamazepine and flunarizine to antagonize voltage-dependent Na+ and Ca2+ channel activitySleep20133690591110.5665/sleep.2722 – reference: WerthEAchermannPDijkDJBorbélyAASpindle frequency activity in the sleep EEG: individual differences and topographic distributionElectroencephalogr. Clin. Neurophysiol.19971035355421:STN:280:DyaK1c%2Fms1Grug%3D%3D10.1016/S0013-4694(97)00070-9 – reference: PlanteDTTopographic and sex-related differences in sleep spindles in major depressive disorder: a high-density EEG investigationJ. Affect. Disord.20131461201251:STN:280:DC%2BC38bms1agsw%3D%3D10.1016/j.jad.2012.06.016 – reference: SitnikovaEHramovAEKoronovskyAAvan LuijtelaarGSleep spindles and spike-wave discharges in EEG: their generic features, similarities and distinctions disclosed with Fourier transform and continuous wavelet analysisJ. Neurosci. Methods200918030431610.1016/j.jneumeth.2009.04.006 – reference: HimanenS-LVirkkalaJHuupponenEHasanJSpindle frequency remains slow in sleep apnea patients throughout the nightSleep Med.2003422923410.1016/S1389-9457(02)00239-3 – reference: SchabusMSleep spindle-related activity in the human EEG and its relation to general cognitive and learning abilitiesEur. J. Neurosci.200623173817461:STN:280:DC%2BD283hslersg%3D%3D10.1111/j.1460-9568.2006.04694.x – reference: FerrarelliFReduced sleep spindle activity in schizophrenia patientsAm. J. Psychiatry200716448349210.1176/ajp.2007.164.3.483 – reference: WamsleyEJReduced sleep spindles and spindle coherence in schizophrenia: mechanisms of impaired memory consolidation?Biol. Psychiatry20127115416110.1016/j.biopsych.2011.08.008 – reference: WendtSLValidation of a novel automatic sleep spindle detector with high performance during sleep in middle aged subjectsConf. Proc. IEEE Eng. Med. Biol. Soc.201220124250425323366866 – reference: SteriadeMGrouping of brain rhythms in corticothalamic systemsNeuroscience2006137108711061:CAS:528:DC%2BD28Xos1emsg%3D%3D10.1016/j.neuroscience.2005.10.029 – reference: SchabusMHemodynamic cerebral correlates of sleep spindles during human non-rapid eye movement sleepProc. Natl. Acad. Sci. 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| Snippet | A comparative analysis of methods for scoring human sleep data, in particular sleep spindles, from encephalographic recordings is reported. The authors develop... Sleep spindles are discrete, intermittent patterns of brain activity observed in human electroencephalographic data. Increasingly, these oscillations are of... Sleep spindles are discrete, intermittent patterns of brain activity that arise as a result of interactions of several circuits in the brain. Increasingly,... |
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| Title | Sleep-spindle detection: crowdsourcing and evaluating performance of experts, non-experts and automated methods |
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