期刊
FRONTIERS IN NEUROSCIENCE
卷 13, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fnins.2019.00207
关键词
machine learning algorithms; polysomnography; signal processing algorithms; sleep stage classification; wake-sleep stage scoring
资金
- Australian International Postgraduate Research Scholarship
- Ministero dell' Universita e della Ricerca Scientifica (MIUR) [2008FY7K9S]
- Australian Research Council [DE140101075]
- Max Planck Society
- University of Zurich
- Wellcome Trust Strategic Award
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre [A90305, A92181]
- National Health and Medical Research Council (NHMRC) Australia Project [APP1012195]
- Australasian Sleep Association, an Australian Postgraduate Award
- Australia and New Zealand Banking Group Limited (ANZ) Trustees Foundation
- Australian Research Council [DE140101075] Funding Source: Australian Research Council
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据