10.3389/fnins.2019.00207.s002 Giancarlo Allocca Giancarlo Allocca Sherie Ma Sherie Ma Davide Martelli Davide Martelli Matteo Cerri Matteo Cerri Flavia Del Vecchio Flavia Del Vecchio Stefano Bastianini Stefano Bastianini Giovanna Zoccoli Giovanna Zoccoli Roberto Amici Roberto Amici Stephen R. Morairty Stephen R. Morairty Anne E. Aulsebrook Anne E. Aulsebrook Shaun Blackburn Shaun Blackburn John A. Lesku John A. Lesku Niels C. Rattenborg Niels C. Rattenborg Alexei L. Vyssotski Alexei L. Vyssotski Emma Wams Emma Wams Kate Porcheret Kate Porcheret Katharina Wulff Katharina Wulff Russell Foster Russell Foster Julia K. M. Chan Julia K. M. Chan Christian L. Nicholas Christian L. Nicholas Dean R. Freestone Dean R. Freestone Leigh A. Johnston Leigh A. Johnston Andrew L. Gundlach Andrew L. Gundlach Table_2_Validation of ‘Somnivore’, a Machine Learning Algorithm for Automated Scoring and Analysis of Polysomnography Data.DOCX Frontiers 2019 machine learning algorithms polysomnography signal processing algorithms sleep stage classification wake–sleep stage scoring 2019-03-18 04:20:08 Dataset https://frontiersin.figshare.com/articles/dataset/Table_2_Validation_of_Somnivore_a_Machine_Learning_Algorithm_for_Automated_Scoring_and_Analysis_of_Polysomnography_Data_DOCX/7856399 <p>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<sup>TM</sup>, 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.</p>