Please use this identifier to cite or link to this item: http://ahro.austin.org.au/austinjspui/handle/1/20633
Title: Validation of 'Somnivore', a Machine Learning Algorithm for Automated Scoring and Analysis of Polysomnography Data.
Authors: Allocca, Giancarlo;Ma, Sherie;Martelli, Davide;Cerri, Matteo;Del Vecchio, Flavia;Bastianini, Stefano;Zoccoli, Giovanna;Amici, Roberto;Morairty, Stephen R;Aulsebrook, Anne E;Blackburn, Shaun;Lesku, John A;Rattenborg, Niels C;Vyssotski, Alexei L;Wams, Emma;Porcheret, Kate;Wulff, Katharina;Foster, Russell;Chan, Julia K M;Nicholas, Christian L;Freestone, Dean R;Johnston, Leigh A;Gundlach, Andrew L
Affiliation: Avian Sleep Group, Max Planck Institute for Ornithology, Seewiesen, Germany
Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
Biomedical Engineering, The University of Melbourne, Parkville, VIC, Australia
Department of Medicine, St. Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia
Institute for Breathing and Sleep, Austin Health, Heidelberg, Victoria, Australia
School of Life Sciences, La Trobe University, Bundoora, VIC, Australia
School of BioSciences, The University of Melbourne, Parkville, VIC, Australia
Laboratory of Autonomic and Behavioral Physiology, Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
The Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia
Somnivore Pty. Ltd., Parkville, VIC, Australia
Laboratory of Autonomic and Behavioral Physiology, Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
PRISM Laboratory, Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
Laboratory of Autonomic and Behavioral Physiology, Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
Center for Neuroscience, Biosciences Division, SRI International, Menlo Park, CA, United States
Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
The Sleep and Circadian Neuroscience Institute (SCNi), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
Issue Date: 18-Mar-2019
EDate: 2019-03-18
Citation: Frontiers in neuroscience 2019; 13: 207
Abstract: 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.
URI: http://ahro.austin.org.au/austinjspui/handle/1/20633
DOI: 10.3389/fnins.2019.00207
PubMed URL: 30936820
ISSN: 1662-4548
Type: Journal Article
Subjects: machine learning algorithms
polysomnography
signal processing algorithms
sleep stage classification
wake–sleep stage scoring
Appears in Collections:Journal articles

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