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Automatic Detection of Microsleep Episodes With Deep Learning

Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs), often subjectively perceived as sleepiness. Their main characteristic is a slowing in frequency in the electroencephalogram (EEG), similar to stage N1 sleep according to standard criteria. The maintenance of wakefu...

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Autores principales: Malafeev, Alexander, Hertig-Godeschalk, Anneke, Schreier, David R., Skorucak, Jelena, Mathis, Johannes, Achermann, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8024556/
https://www.ncbi.nlm.nih.gov/pubmed/33841068
http://dx.doi.org/10.3389/fnins.2021.564098
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author Malafeev, Alexander
Hertig-Godeschalk, Anneke
Schreier, David R.
Skorucak, Jelena
Mathis, Johannes
Achermann, Peter
author_facet Malafeev, Alexander
Hertig-Godeschalk, Anneke
Schreier, David R.
Skorucak, Jelena
Mathis, Johannes
Achermann, Peter
author_sort Malafeev, Alexander
collection PubMed
description Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs), often subjectively perceived as sleepiness. Their main characteristic is a slowing in frequency in the electroencephalogram (EEG), similar to stage N1 sleep according to standard criteria. The maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance. Scoring of the MWT in most sleep-wake centers is limited to classical definition of sleep (30 s epochs), and MSEs are mostly not considered in the absence of established scoring criteria defining MSEs but also because of the laborious work. We aimed for automatic detection of MSEs with machine learning, i.e., with deep learning based on raw EEG and EOG data as input. We analyzed MWT data of 76 patients. Experts visually scored wakefulness, and according to recently developed scoring criteria MSEs, microsleep episode candidates (MSEc), and episodes of drowsiness (ED). We implemented segmentation algorithms based on convolutional neural networks (CNNs) and a combination of a CNN with a long-short term memory (LSTM) network. A LSTM network is a type of a recurrent neural network which has a memory for past events and takes them into account. Data of 53 patients were used for training of the classifiers, 12 for validation and 11 for testing. Our algorithms showed a good performance close to human experts. The detection was very good for wakefulness and MSEs and poor for MSEc and ED, similar to the low inter-expert reliability for these borderline segments. We performed a visualization of the internal representation of the data by the artificial neuronal network performing best using t-distributed stochastic neighbor embedding (t-SNE). Visualization revealed that MSEs and wakefulness were mostly separable, though not entirely, and MSEc and ED largely intersected with the two main classes. We provide a proof of principle that it is feasible to reliably detect MSEs with deep neuronal networks based on raw EEG and EOG data with a performance close to that of human experts. The code of the algorithms (https://github.com/alexander-malafeev/microsleep-detection) and data (https://zenodo.org/record/3251716) are available.
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spelling pubmed-80245562021-04-08 Automatic Detection of Microsleep Episodes With Deep Learning Malafeev, Alexander Hertig-Godeschalk, Anneke Schreier, David R. Skorucak, Jelena Mathis, Johannes Achermann, Peter Front Neurosci Neuroscience Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs), often subjectively perceived as sleepiness. Their main characteristic is a slowing in frequency in the electroencephalogram (EEG), similar to stage N1 sleep according to standard criteria. The maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance. Scoring of the MWT in most sleep-wake centers is limited to classical definition of sleep (30 s epochs), and MSEs are mostly not considered in the absence of established scoring criteria defining MSEs but also because of the laborious work. We aimed for automatic detection of MSEs with machine learning, i.e., with deep learning based on raw EEG and EOG data as input. We analyzed MWT data of 76 patients. Experts visually scored wakefulness, and according to recently developed scoring criteria MSEs, microsleep episode candidates (MSEc), and episodes of drowsiness (ED). We implemented segmentation algorithms based on convolutional neural networks (CNNs) and a combination of a CNN with a long-short term memory (LSTM) network. A LSTM network is a type of a recurrent neural network which has a memory for past events and takes them into account. Data of 53 patients were used for training of the classifiers, 12 for validation and 11 for testing. Our algorithms showed a good performance close to human experts. The detection was very good for wakefulness and MSEs and poor for MSEc and ED, similar to the low inter-expert reliability for these borderline segments. We performed a visualization of the internal representation of the data by the artificial neuronal network performing best using t-distributed stochastic neighbor embedding (t-SNE). Visualization revealed that MSEs and wakefulness were mostly separable, though not entirely, and MSEc and ED largely intersected with the two main classes. We provide a proof of principle that it is feasible to reliably detect MSEs with deep neuronal networks based on raw EEG and EOG data with a performance close to that of human experts. The code of the algorithms (https://github.com/alexander-malafeev/microsleep-detection) and data (https://zenodo.org/record/3251716) are available. Frontiers Media S.A. 2021-03-24 /pmc/articles/PMC8024556/ /pubmed/33841068 http://dx.doi.org/10.3389/fnins.2021.564098 Text en Copyright © 2021 Malafeev, Hertig-Godeschalk, Schreier, Skorucak, Mathis and Achermann. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Malafeev, Alexander
Hertig-Godeschalk, Anneke
Schreier, David R.
Skorucak, Jelena
Mathis, Johannes
Achermann, Peter
Automatic Detection of Microsleep Episodes With Deep Learning
title Automatic Detection of Microsleep Episodes With Deep Learning
title_full Automatic Detection of Microsleep Episodes With Deep Learning
title_fullStr Automatic Detection of Microsleep Episodes With Deep Learning
title_full_unstemmed Automatic Detection of Microsleep Episodes With Deep Learning
title_short Automatic Detection of Microsleep Episodes With Deep Learning
title_sort automatic detection of microsleep episodes with deep learning
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8024556/
https://www.ncbi.nlm.nih.gov/pubmed/33841068
http://dx.doi.org/10.3389/fnins.2021.564098
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