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A two-stage learning convolutional neural network for sleep stage classification using a filterbank and single feature

Sleep is an essential process for the body that helps to maintain its health and vitality. The first stage in the diagnosis and treatment of sleep disorders is sleep staging. Due to the complications in manual sleep staging by the physician, computer-aided sleep stage classification algorithms are g...

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Main Authors: Mehdi Abdollahpour, Tohid Yousefi Rezaii, Ali Farzamnia, Ismail Saad
Format: Article
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. (IEEE) 2022
Subjects:
QA71-90 Instruments and machines
RC31-1245 Internal medicine
Online Access:https://eprints.ums.edu.my/id/eprint/34047/1/A%20Two-Stage%20Learning%20Convolutional%20Neural%20Network%20for%20Sleep%20Stage%20Classification%20Using%20a%20Filterbank%20and%20Single%20Feature%20%282%29.pdf
https://eprints.ums.edu.my/id/eprint/34047/2/A%20Two-Stage%20Learning%20Convolutional%20Neural%20Network%20for%20Sleep%20Stage%20Classification%20Using%20a%20Filterbank%20and%20Single%20Feature%20_ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/34047/
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9791443
https://doi.org/10.1109/ACCESS.2022.3180730
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spelling my.ums.eprints.340472022-08-29T03:28:36Z https://eprints.ums.edu.my/id/eprint/34047/ A two-stage learning convolutional neural network for sleep stage classification using a filterbank and single feature Mehdi Abdollahpour Tohid Yousefi Rezaii Ali Farzamnia Ismail Saad QA71-90 Instruments and machines RC31-1245 Internal medicine Sleep is an essential process for the body that helps to maintain its health and vitality. The first stage in the diagnosis and treatment of sleep disorders is sleep staging. Due to the complications in manual sleep staging by the physician, computer-aided sleep stage classification algorithms are gaining attention. In this study, a novel approach was introduced to extract distinctive representations from single-channel EEG signal for automatic sleep staging. Standard deviation as a single feature was extracted from the frequency subbands of EEG, which gave a comprehensive understanding of the signal and its activity within various frequency ranges for different sleep stages. The features formed the input space of the proposed two-stream convolutional neural network (CNN) for classification and two-stage learning was used to train the model that achieved improvements in terms of accuracy, reliability and robustness against traditional classifiers and conventional training method of the neural networks. For the performance evaluation, three well-known benchmark datasets including Sleep EDF, Sleep EDFx and DREAMS Subject were used. The proposed algorithm by utilizing simple and effective methods improved sleep stage classification results by achieving an overall accuracy of 93.48%, 93.14% and 83.55%, respectively. The introduced framework in this study has great potential for practical implementation on a home-based sleep staging device. Institute of Electrical and Electronics Engineers Inc. (IEEE) 2022 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/34047/1/A%20Two-Stage%20Learning%20Convolutional%20Neural%20Network%20for%20Sleep%20Stage%20Classification%20Using%20a%20Filterbank%20and%20Single%20Feature%20%282%29.pdf text en https://eprints.ums.edu.my/id/eprint/34047/2/A%20Two-Stage%20Learning%20Convolutional%20Neural%20Network%20for%20Sleep%20Stage%20Classification%20Using%20a%20Filterbank%20and%20Single%20Feature%20_ABSTRACT.pdf Mehdi Abdollahpour and Tohid Yousefi Rezaii and Ali Farzamnia and Ismail Saad (2022) A two-stage learning convolutional neural network for sleep stage classification using a filterbank and single feature. IEEE Access, 10. pp. 60597-60609. ISSN 2169-3536 https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9791443 https://doi.org/10.1109/ACCESS.2022.3180730
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic QA71-90 Instruments and machines
RC31-1245 Internal medicine
spellingShingle QA71-90 Instruments and machines
RC31-1245 Internal medicine
Mehdi Abdollahpour
Tohid Yousefi Rezaii
Ali Farzamnia
Ismail Saad
A two-stage learning convolutional neural network for sleep stage classification using a filterbank and single feature
description Sleep is an essential process for the body that helps to maintain its health and vitality. The first stage in the diagnosis and treatment of sleep disorders is sleep staging. Due to the complications in manual sleep staging by the physician, computer-aided sleep stage classification algorithms are gaining attention. In this study, a novel approach was introduced to extract distinctive representations from single-channel EEG signal for automatic sleep staging. Standard deviation as a single feature was extracted from the frequency subbands of EEG, which gave a comprehensive understanding of the signal and its activity within various frequency ranges for different sleep stages. The features formed the input space of the proposed two-stream convolutional neural network (CNN) for classification and two-stage learning was used to train the model that achieved improvements in terms of accuracy, reliability and robustness against traditional classifiers and conventional training method of the neural networks. For the performance evaluation, three well-known benchmark datasets including Sleep EDF, Sleep EDFx and DREAMS Subject were used. The proposed algorithm by utilizing simple and effective methods improved sleep stage classification results by achieving an overall accuracy of 93.48%, 93.14% and 83.55%, respectively. The introduced framework in this study has great potential for practical implementation on a home-based sleep staging device.
format Article
author Mehdi Abdollahpour
Tohid Yousefi Rezaii
Ali Farzamnia
Ismail Saad
author_facet Mehdi Abdollahpour
Tohid Yousefi Rezaii
Ali Farzamnia
Ismail Saad
author_sort Mehdi Abdollahpour
title A two-stage learning convolutional neural network for sleep stage classification using a filterbank and single feature
title_short A two-stage learning convolutional neural network for sleep stage classification using a filterbank and single feature
title_full A two-stage learning convolutional neural network for sleep stage classification using a filterbank and single feature
title_fullStr A two-stage learning convolutional neural network for sleep stage classification using a filterbank and single feature
title_full_unstemmed A two-stage learning convolutional neural network for sleep stage classification using a filterbank and single feature
title_sort two-stage learning convolutional neural network for sleep stage classification using a filterbank and single feature
publisher Institute of Electrical and Electronics Engineers Inc. (IEEE)
publishDate 2022
url https://eprints.ums.edu.my/id/eprint/34047/1/A%20Two-Stage%20Learning%20Convolutional%20Neural%20Network%20for%20Sleep%20Stage%20Classification%20Using%20a%20Filterbank%20and%20Single%20Feature%20%282%29.pdf
https://eprints.ums.edu.my/id/eprint/34047/2/A%20Two-Stage%20Learning%20Convolutional%20Neural%20Network%20for%20Sleep%20Stage%20Classification%20Using%20a%20Filterbank%20and%20Single%20Feature%20_ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/34047/
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9791443
https://doi.org/10.1109/ACCESS.2022.3180730
_version_ 1760231243003199488
score 13.211869

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