Automatic detection of microsleep episodes with feature-based machine learning
OPEN ACCESS
Loading...
Author / Producer
Date
2020-01
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
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.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
43 (1)
Pages / Article No.
Publisher
Oxford University Press
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
microsleep; excessive daytime sleepiness; vigilance assessment; maintenance of wakefulness test; machine learning
Organisational unit
Notes
It was possible to publish this article open access thanks to a Swiss National Licence with the publisher.