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.

Detection of REM sleep behaviour disorder by automated polysomnography analysis

“…Furthermore the EMG signal helps distinguish REM from W in HCs, but this attribute is often not helpful in the context of RBD, where there can be an absence of atonia in REM. Critical to RBD diagnosis is the identification of REM sleep, and while other studies in automated sleep staging produce better results in REM sleep detection, they benefit from primarily focusing on young HCs or a relatively smaller sample size (Virkkala et al 2008;Güneş et al 2010;Fraiwan et al 2012;Kempfner et al 2012Kempfner et al , 2013bLiang et al 2012;Bajaj and Pachori 2013;Khalighi et al 2013;Imtiaz and Rodriguez-Villegas 2014;McCarty et al 2014;Sousa et al 2015;Lajnef et al 2015;Yetton et al 2016). Despite the lack of sensitivity, REM specificity remains high, which means that as long as REM is identified with a certain precision, the quantified absence of atonia will remain indicative.…”

“…The literature on automated sleep staging describes numerous features, which provided the basis for this study and are summarised in Table 2. For this study the pre-processed EEG, EOG, and EMG signals were segmented into 10 second mini-epochs in order to calculate features for each 30-second epoch, a technique often used for sleep stage classification literature (Güneş et al 2010;Koley and Dey 2012;Liang et al 2012;Lajnef et al 2015;Yetton et al 2016).…”

“…There are numerous methods in the literature that utilise PSG recordings to classify REM stages either specifically (Kempfner et al 2012;Imtiaz and Rodriguez-Villegas 2014;McCarty et al 2014;Yetton et al 2016) or as part of multi-sleep-stage classification (Virkkala et al 2008;Güneş et al 2010;Fraiwan et al 2012;Liang et al 2012;Bajaj and Pachori 2013;Kempfner et al 2013b;Khalighi et al 2013;Lajnef et al 2015;Sousa et al 2015). Many of these automated sleep scoring algorithms produce results that are comparable to expert annotation of PSG recordings from young healthy controls.…”

P(Sleep) 2.0: Capturing the dynamics of nighttime sleep using probabilistic models and big data

“…Finally, Chapter 4 focuses on the methods and results of P(Sleep) 2.0 and discusses its limitations and possible future directions. Chapters 2 and 3 summarize previous peer-reviewed works by myself (Yetton, McDevitt, Cellini, Shelton, & Mednick, 2018;Yetton et al, 2016), and finer methodological details can be found in their respective papers.…”

“…The follow chapter introduces a mathematical model of sleep known as "P(Sleep) 2.0", and combines previous work of the author (a model of sleep architecture known as "P(Sleep) 1.0", and an automatic sleep feature detector, (Yetton et al, 2016)…”