Automatic detection of rapid eye movements (REMs): A machine learning approach

Abstract

Background: 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.

New method: 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.

Results: 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).

Comparison with existing method(s): By following reported methods, we implemented all previously published LOC and ROC based detection algorithms on our dataset. Our algorithm performance exceeded all others.

Conclusions: 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.

Keywords: Adaptive boosting; EEG; LOC; Machine learning; Polysomnography; REM density; REM detection; ROC; Sleep scoring.