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Deep Recurrent Neural Networks for Automatic Detection of Sleep Apnea from Single Channel Respiration Signals

Sleep apnea is a common sleep disorder that causes repeated breathing interruption during sleep. The performance of automated apnea detection methods based on respiratory signals depend on the signals considered and feature extraction methods. Moreover, feature engineering techniques are highly depe...

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Autores principales: ElMoaqet, Hisham, Eid, Mohammad, Glos, Martin, Ryalat, Mutaz, Penzel, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570636/
https://www.ncbi.nlm.nih.gov/pubmed/32899819
http://dx.doi.org/10.3390/s20185037
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author ElMoaqet, Hisham
Eid, Mohammad
Glos, Martin
Ryalat, Mutaz
Penzel, Thomas
author_facet ElMoaqet, Hisham
Eid, Mohammad
Glos, Martin
Ryalat, Mutaz
Penzel, Thomas
author_sort ElMoaqet, Hisham
collection PubMed
description Sleep apnea is a common sleep disorder that causes repeated breathing interruption during sleep. The performance of automated apnea detection methods based on respiratory signals depend on the signals considered and feature extraction methods. Moreover, feature engineering techniques are highly dependent on the experts’ experience and their prior knowledge about different physiological signals and conditions of the subjects. To overcome these problems, a novel deep recurrent neural network (RNN) framework is developed for automated feature extraction and detection of apnea events from single respiratory channel inputs. Long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) are investigated to develop the proposed deep RNN model. The proposed framework is evaluated over three respiration signals: Oronasal thermal airflow (FlowTh), nasal pressure (NPRE), and abdominal respiratory inductance plethysmography (ABD). To demonstrate our results, we use polysomnography (PSG) data of 17 patients with obstructive, central, and mixed apnea events. Our results indicate the effectiveness of the proposed framework in automatic extraction for temporal features and automated detection of apneic events over the different respiratory signals considered in this study. Using a deep BiLSTM-based detection model, the NPRE signal achieved the highest overall detection results with true positive rate (sensitivity) = 90.3%, true negative rate (specificity) = 83.7%, and area under receiver operator characteristic curve = 92.4%. The present results contribute a new deep learning approach for automated detection of sleep apnea events from single channel respiration signals that can potentially serve as a helpful and alternative tool for the traditional PSG method.
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spelling pubmed-75706362020-10-28 Deep Recurrent Neural Networks for Automatic Detection of Sleep Apnea from Single Channel Respiration Signals ElMoaqet, Hisham Eid, Mohammad Glos, Martin Ryalat, Mutaz Penzel, Thomas Sensors (Basel) Article Sleep apnea is a common sleep disorder that causes repeated breathing interruption during sleep. The performance of automated apnea detection methods based on respiratory signals depend on the signals considered and feature extraction methods. Moreover, feature engineering techniques are highly dependent on the experts’ experience and their prior knowledge about different physiological signals and conditions of the subjects. To overcome these problems, a novel deep recurrent neural network (RNN) framework is developed for automated feature extraction and detection of apnea events from single respiratory channel inputs. Long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) are investigated to develop the proposed deep RNN model. The proposed framework is evaluated over three respiration signals: Oronasal thermal airflow (FlowTh), nasal pressure (NPRE), and abdominal respiratory inductance plethysmography (ABD). To demonstrate our results, we use polysomnography (PSG) data of 17 patients with obstructive, central, and mixed apnea events. Our results indicate the effectiveness of the proposed framework in automatic extraction for temporal features and automated detection of apneic events over the different respiratory signals considered in this study. Using a deep BiLSTM-based detection model, the NPRE signal achieved the highest overall detection results with true positive rate (sensitivity) = 90.3%, true negative rate (specificity) = 83.7%, and area under receiver operator characteristic curve = 92.4%. The present results contribute a new deep learning approach for automated detection of sleep apnea events from single channel respiration signals that can potentially serve as a helpful and alternative tool for the traditional PSG method. MDPI 2020-09-04 /pmc/articles/PMC7570636/ /pubmed/32899819 http://dx.doi.org/10.3390/s20185037 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
ElMoaqet, Hisham
Eid, Mohammad
Glos, Martin
Ryalat, Mutaz
Penzel, Thomas
Deep Recurrent Neural Networks for Automatic Detection of Sleep Apnea from Single Channel Respiration Signals
title Deep Recurrent Neural Networks for Automatic Detection of Sleep Apnea from Single Channel Respiration Signals
title_full Deep Recurrent Neural Networks for Automatic Detection of Sleep Apnea from Single Channel Respiration Signals
title_fullStr Deep Recurrent Neural Networks for Automatic Detection of Sleep Apnea from Single Channel Respiration Signals
title_full_unstemmed Deep Recurrent Neural Networks for Automatic Detection of Sleep Apnea from Single Channel Respiration Signals
title_short Deep Recurrent Neural Networks for Automatic Detection of Sleep Apnea from Single Channel Respiration Signals
title_sort deep recurrent neural networks for automatic detection of sleep apnea from single channel respiration signals
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570636/
https://www.ncbi.nlm.nih.gov/pubmed/32899819
http://dx.doi.org/10.3390/s20185037
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