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Contributors:
  1. Mohammad Niknazar
  2. Lauren N. Whitehurst
  3. Sara C. Mednick

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Description: Rapid eye movements (REMs) are a defining feature of REM sleep. The number of discrete REMs over time, or REM density, has been investigated as a marker of clinical psychopathology and memory consolidation. However, human detection of REMs is a time-consuming and subjective process. Therefore, reliable, automated REM detection software is a valuable research tool. We developed an automatic REM detection algorithm combining a novel set of extracted features and the 'AdaBoost' classification algorithm to detect the presence of REMs in Electrooculogram data collected from the right and left outer canthi (ROC/LOC). Algorithm performance measures of Recall (percentage of REMs detected) and Precision (percentage of REMs detected that are true REMs) were calculated and compared to the gold standard of human detection by three expert sleep scorers. REM detection by four non-experts were also investigated and compared to expert raters and the algorithm. The algorithm performance (78.1% Recall, 82.6% Precision) surpassed that of the average (expert & non-expert) single human detection performance (76% Recall, 83% Precision). Agreement between non-experts (Cronbach Alpha=0.65) is markedly lower than experts (Cronbach Alpha=0.80). By following reported methods, we implemented all previously published LOC and ROC based detection algorithms on our dataset. Our algorithm performance exceeded all others. The automatic detection algorithm presented is a viable and efficient method of REM detection as it reliably matches the performance of human scorers and outperforms all other known LOC- and ROC-based detection algorithms. DOI: 10.1016/j.jneumeth.2015.11.015 Link to publication: http://www.sciencedirect.com/science/article/pii/S0165027015004173

License: BSD 3-Clause "New"/"Revised" License

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Automatic REM detector

v0.1

This repository contains the MATLAB code for the Automatic REM Detector (For LOC and ROC) developed by Yetton et al.

DOI: 10.1016/j.jneumeth.2015.11.015

It is suggested to read the publication before beginning, specifically sections 2.1 and 4.0 to understand data setup and limitations before using these algorithms. Please use the GitHub page for all feature suggestions,…

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Tags

AdaBoostMachine LearningPolysomnographyRapid Eye MovementREM DensityREM Detection

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