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Validation of 'Somn...
Validation of 'Somnivore', a Machine Learning Algorithm for Automated Scoring and Analysis of Polysomnography Data
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Allocca, Giancarlo (author)
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Ma, Sherie (author)
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Martelli, Davide (author)
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Cerri, Matteo (author)
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Del Vecchio, Flavia (author)
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Bastianini, Stefano (author)
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Zoccoli, Giovanna (author)
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Amici, Roberto (author)
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Morairty, Stephen R. (author)
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Aulsebrook, Anne E. (author)
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Blackburn, Shaun (author)
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Lesku, John A. (author)
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Rattenborg, Niels C. (author)
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Vyssotski, Alexei L. (author)
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Wams, Emma (author)
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Porcherer, Kate (author)
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- Wulff, Katharina (author)
- Umeå universitet,Diagnostisk radiologi,Institutionen för molekylärbiologi (Medicinska fakulteten),Centre for Molecular Medicine (WCMM), Umeå University, Sweden
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Foster, Russell (author)
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Chan, Julia K. M. (author)
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Nicholas, Christian L. (author)
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Freestone, Dean R. (author)
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Johnston, Leigh A. (author)
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Gundlachla, Andrew L. (author)
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(creator_code:org_t)
- 2019-03-18
- 2019
- English.
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In: Frontiers in Neuroscience. - : Frontiers Media S.A.. - 1662-4548 .- 1662-453X. ; 13
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
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- Manual scoring of polysomnography data is labor-intensive and time-consuming, and most existing software does not account for subjective differences and user variability. Therefore, we evaluated a supervised machine learning algorithm, Somnivore (TM), for automated wake-sleep stage classification. We designed an algorithm that extracts features from various input channels, following a brief session of manual scoring, and provides automated wake-sleep stage classification for each recording. For algorithm validation, polysomnography data was obtained from independent laboratories, and include normal, cognitively-impaired, and alcohol-treated human subjects (total n = 52), narcoleptic mice and drug-treated rats (total n = 56), and pigeons (n = 5). Training and testing sets for validation were previously scored manually by 1-2 trained sleep technologists from each laboratory. F-measure was used to assess precision and sensitivity for statistical analysis of classifier output and human scorer agreement. The algorithm gave high concordance with manual visual scoring across all human data (wake 0.91 +/- 0.01; N1 0.57 +/- 0.01; N2 0.81 +/- 0.01; N3 0.86 +/- 0.01; REM 0.87 +/- 0.01), which was comparable to manual inter-scorer agreement on all stages. Similarly, high concordance was observed across all rodent (wake 0.95 +/- 0.01; NREM 0.94 +/- 0.01; REM 0.91 +/- 0.01) and pigeon (wake 0.96 +/- 0.006; NREM 0.97 +/- 0.01; REM 0.86 +/- 0.02) data. Effects of classifier learning from single signal inputs, simple stage reclassification, automated removal of transition epochs, and training set size were also examined. In summary, we have developed a polysomnography analysis program for automated sleep-stage classification of data from diverse species. Somnivore enables flexible, accurate, and high-throughput analysis of experimental and clinical sleep studies.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Neurovetenskaper (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Neurosciences (hsv//eng)
Keyword
- machine learning algorithms
- polysomnography
- signal processing algorithms
- sleep stage classification
- wake-sleep stage scoring
Publication and Content Type
- ref (subject category)
- art (subject category)
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- By the author/editor
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Allocca, Giancar ...
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Ma, Sherie
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Martelli, Davide
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Cerri, Matteo
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Del Vecchio, Fla ...
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Bastianini, Stef ...
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show more...
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Zoccoli, Giovann ...
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Amici, Roberto
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Morairty, Stephe ...
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Aulsebrook, Anne ...
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Blackburn, Shaun
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Lesku, John A.
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Rattenborg, Niel ...
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Vyssotski, Alexe ...
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Wams, Emma
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Porcherer, Kate
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Wulff, Katharina
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Foster, Russell
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Chan, Julia K. M ...
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Nicholas, Christ ...
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Freestone, Dean ...
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Johnston, Leigh ...
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Gundlachla, Andr ...
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- About the subject
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- MEDICAL AND HEALTH SCIENCES
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MEDICAL AND HEAL ...
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and Basic Medicine
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and Neurosciences
- Articles in the publication
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Frontiers in Neu ...
- By the university
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Umeå University