Automatic detection of microsleep episodes with feature-based machine learning. Issue 1 (27th September 2019)

• Record Type: Journal Article
• Authors: Skorucak, Jelena; Hertig-Godeschalk, Anneke; Schreier, David R; Malafeev, Alexander; Mathis, Johannes; Achermann, Peter
• Is Part Of: Sleep. Volume 43:Issue 1(2020)
• Publication Date: 2019-09-27
• Subjects: microsleep -- excessive daytime sleepiness -- vigilance assessment -- maintenance of wakefulness test -- machine learning; Sleep -- Physiological aspects -- Periodicals; Sleep disorders -- Periodicals; Sommeil -- Aspect physiologique -- Périodiques; Sommeil, Troubles du -- Périodiques; Sleep disorders; Sleep -- Physiological aspects; Sleep -- physiological aspects; Sleep Wake Disorders; Psychophysiology; Electronic journals; Periodicals; 616.8498
• DOI: 10.1093/sleep/zsz225
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Study Objectives: Microsleep episodes (MSEs) are brief episodes of sleep, mostly defined to be shorter than 15 s. In the electroencephalogram (EEG), MSEs are mainly characterized by a slowing in frequency. The identification of early signs of sleepiness and sleep (e.g. MSEs) is of considerable clinical and practical relevance. Under laboratory conditions, the maintenance of wakefulness test (MWT) is often used for assessing vigilance. Methods: We analyzed MWT recordings of 76 patients referred to the Sleep-Wake-Epilepsy-Center. MSEs were scored by experts defined by the occurrence of theta dominance on ≥1 occipital derivation lasting 1–15 s, whereas the eyes were at least 80% closed. We calculated spectrograms using an autoregressive model of order 16 of 1 s epochs moved in 200 ms steps in order to visualize oscillatory activity and derived seven features per derivation: power in delta, theta, alpha and beta bands, ratio theta/(alpha + beta), quantified eye movements, and median frequency. Three algorithms were used for MSE classification: support vector machine (SVM), random forest (RF), and an artificial neural network (long short-term memory [LSTM] network). Data of 53 patients were used for the training of the classifiers, and 23 for testing. Results: MSEs were identified with a high performance (sensitivity, specificity, precision, accuracy, and Cohen's kappa coefficient). Training revealed that delta power and the ratio theta/(alpha + beta) were most relevant features for the RF classifier and eye movements for the LSTM network. Conclusions: The automatic detection of MSEs was successful for our EEG-based definition of MSEs, with good performance of all algorithms applied.