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Automatic detection of microsleep episodes with feature-based machine learning


Skorucak, Jelena; Hertig-Godeschalk, Anneke; Schreier, David R; Malafeev, Alexander; Mathis, Johannes; Achermann, Peter (2019). Automatic detection of microsleep episodes with feature-based machine learning. Sleep:zsz225.

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, while 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.

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, while 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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Pharmacology and Toxicology
07 Faculty of Science > Institute of Pharmacology and Toxicology

04 Faculty of Medicine > Psychiatric University Hospital Zurich > Clinic for Psychiatry, Psychotherapy, and Psychosomatics
04 Faculty of Medicine > The KEY Institute for Brain-Mind Research
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Uncontrolled Keywords:excessive daytime sleepiness; machine learning; maintenance of wakefulness test; microsleep; vigilance assessment
Language:English
Date:27 September 2019
Deposited On:11 Nov 2019 10:57
Last Modified:23 Jan 2020 07:36
Publisher:American Academy of Sleep Medicine
ISSN:0161-8105
OA Status:Closed
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1093/sleep/zsz225
PubMed ID:31559424

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Embargo till: 2021-01-13