Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Validation of 'Somnivore', a Machine Learning Algorithm for Automated Scoring and Analysis of Polysomnography Data
Vise andre og tillknytning
2019 (engelsk)Inngår i: Frontiers in Neuroscience, ISSN 1662-4548, E-ISSN 1662-453X, Vol. 13, artikkel-id 207Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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.

sted, utgiver, år, opplag, sider
Frontiers Media S.A., 2019. Vol. 13, artikkel-id 207
Emneord [en]
machine learning algorithms, polysomnography, signal processing algorithms, sleep stage classification, wake-sleep stage scoring
HSV kategori
Identifikatorer
URN: urn:nbn:se:umu:diva-162495DOI: 10.3389/fnins.2019.00207ISI: 000461625200001PubMedID: 30936820OAI: oai:DiVA.org:umu-162495DiVA, id: diva2:1344920
Tilgjengelig fra: 2019-08-22 Laget: 2019-08-22 Sist oppdatert: 2019-08-22bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstPubMed

Søk i DiVA

Av forfatter/redaktør
Martelli, DavideWulff, KatharinaJohnston, Leigh A.Gundlachla, Andrew L.
Av organisasjonen
I samme tidsskrift
Frontiers in Neuroscience

Søk utenfor DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric

doi
pubmed
urn-nbn
Totalt: 108 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf