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