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Validation of 'Somnivore', a Machine Learning Algorithm for Automated Scoring and Analysis of Polysomnography Data.

  • Allocca G The Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia.
  • Ma S The Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia.
  • Martelli D The Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia.
  • Cerri M Laboratory of Autonomic and Behavioral Physiology, Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
  • Del Vecchio F Laboratory of Autonomic and Behavioral Physiology, Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
  • Bastianini S PRISM Laboratory, Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
  • Zoccoli G PRISM Laboratory, Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
  • Amici R Laboratory of Autonomic and Behavioral Physiology, Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
  • Morairty SR Center for Neuroscience, Biosciences Division, SRI International, Menlo Park, CA, United States.
  • Aulsebrook AE School of BioSciences, The University of Melbourne, Parkville, VIC, Australia.
  • Blackburn S School of Life Sciences, La Trobe University, Bundoora, VIC, Australia.
  • Lesku JA School of Life Sciences, La Trobe University, Bundoora, VIC, Australia.
  • Rattenborg NC Avian Sleep Group, Max Planck Institute for Ornithology, Seewiesen, Germany.
  • Vyssotski AL Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.
  • Wams E The Sleep and Circadian Neuroscience Institute (SCNi), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
  • Porcheret K The Sleep and Circadian Neuroscience Institute (SCNi), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
  • Wulff K The Sleep and Circadian Neuroscience Institute (SCNi), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
  • Foster R The Sleep and Circadian Neuroscience Institute (SCNi), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
  • Chan JKM Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia.
  • Nicholas CL Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia.
  • Freestone DR Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia.
  • Johnston LA Biomedical Engineering, The University of Melbourne, Parkville, VIC, Australia.
  • Gundlach AL The Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia.
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  • 2019-04-03
Published in:
  • Frontiers in neuroscience. - 2019
machine learning algorithms
polysomnography
signal processing algorithms
sleep stage classification
wake–sleep stage scoring
English 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, SomnivoreTM, 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.
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  • English
Open access status
gold
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https://sonar.ch/global/documents/61418
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