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A Holistic Strategy for Classification of Sleep Stages with EEG

Manual sleep stage scoring is usually implemented with the help of sleep specialists by means of visual inspection of the neurophysiological signals of the patient. As it is a very hectic task to perform, automated sleep stage classification systems were developed in the past, and advancements are b...

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Autores principales: Prabhakar, Sunil Kumar, Rajaguru, Harikumar, Ryu, Semin, Jeong, In cheol, Won, Dong-Ok
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9103466/
https://www.ncbi.nlm.nih.gov/pubmed/35591246
http://dx.doi.org/10.3390/s22093557
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author Prabhakar, Sunil Kumar
Rajaguru, Harikumar
Ryu, Semin
Jeong, In cheol
Won, Dong-Ok
author_facet Prabhakar, Sunil Kumar
Rajaguru, Harikumar
Ryu, Semin
Jeong, In cheol
Won, Dong-Ok
author_sort Prabhakar, Sunil Kumar
collection PubMed
description Manual sleep stage scoring is usually implemented with the help of sleep specialists by means of visual inspection of the neurophysiological signals of the patient. As it is a very hectic task to perform, automated sleep stage classification systems were developed in the past, and advancements are being made consistently by researchers. The various stages of sleep are identified by these automated sleep stage classification systems, and it is quite an important step to assist doctors for the diagnosis of sleep-related disorders. In this work, a holistic strategy named as clustering and dimensionality reduction with feature extraction cum selection for classification along with deep learning (CDFCD) is proposed for the classification of sleep stages with EEG signals. Though the methodology follows a similar structural flow as proposed in the past works, many advanced and novel techniques are proposed under each category in this work flow. Initially, clustering is applied with the help of hierarchical clustering, spectral clustering, and the proposed principal component analysis (PCA)-based subspace clustering. Then the dimensionality of it is reduced with the help of the proposed singular value decomposition (SVD)-based spectral algorithm and the standard variational Bayesian matrix factorization (VBMF) technique. Then the features are extracted and selected with the two novel proposed techniques, such as the sparse group lasso technique with dual-level implementation (SGL-DLI) and the ridge regression technique with limiting weight scheme (RR-LWS). Finally, the classification happens with the less explored multiclass Gaussian process classification (MGC), the proposed random arbitrary collective classification (RACC), and the deep learning technique using long short-term memory (LSTM) along with other conventional machine learning techniques. This methodology is validated on the sleep EDF database, and the results obtained with this methodology have surpassed the results of the previous studies in terms of the obtained classification accuracy reporting a high accuracy of 93.51% even for the six-classes classification problem.
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spelling pubmed-91034662022-05-14 A Holistic Strategy for Classification of Sleep Stages with EEG Prabhakar, Sunil Kumar Rajaguru, Harikumar Ryu, Semin Jeong, In cheol Won, Dong-Ok Sensors (Basel) Article Manual sleep stage scoring is usually implemented with the help of sleep specialists by means of visual inspection of the neurophysiological signals of the patient. As it is a very hectic task to perform, automated sleep stage classification systems were developed in the past, and advancements are being made consistently by researchers. The various stages of sleep are identified by these automated sleep stage classification systems, and it is quite an important step to assist doctors for the diagnosis of sleep-related disorders. In this work, a holistic strategy named as clustering and dimensionality reduction with feature extraction cum selection for classification along with deep learning (CDFCD) is proposed for the classification of sleep stages with EEG signals. Though the methodology follows a similar structural flow as proposed in the past works, many advanced and novel techniques are proposed under each category in this work flow. Initially, clustering is applied with the help of hierarchical clustering, spectral clustering, and the proposed principal component analysis (PCA)-based subspace clustering. Then the dimensionality of it is reduced with the help of the proposed singular value decomposition (SVD)-based spectral algorithm and the standard variational Bayesian matrix factorization (VBMF) technique. Then the features are extracted and selected with the two novel proposed techniques, such as the sparse group lasso technique with dual-level implementation (SGL-DLI) and the ridge regression technique with limiting weight scheme (RR-LWS). Finally, the classification happens with the less explored multiclass Gaussian process classification (MGC), the proposed random arbitrary collective classification (RACC), and the deep learning technique using long short-term memory (LSTM) along with other conventional machine learning techniques. This methodology is validated on the sleep EDF database, and the results obtained with this methodology have surpassed the results of the previous studies in terms of the obtained classification accuracy reporting a high accuracy of 93.51% even for the six-classes classification problem. MDPI 2022-05-07 /pmc/articles/PMC9103466/ /pubmed/35591246 http://dx.doi.org/10.3390/s22093557 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Prabhakar, Sunil Kumar
Rajaguru, Harikumar
Ryu, Semin
Jeong, In cheol
Won, Dong-Ok
A Holistic Strategy for Classification of Sleep Stages with EEG
title A Holistic Strategy for Classification of Sleep Stages with EEG
title_full A Holistic Strategy for Classification of Sleep Stages with EEG
title_fullStr A Holistic Strategy for Classification of Sleep Stages with EEG
title_full_unstemmed A Holistic Strategy for Classification of Sleep Stages with EEG
title_short A Holistic Strategy for Classification of Sleep Stages with EEG
title_sort holistic strategy for classification of sleep stages with eeg
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9103466/
https://www.ncbi.nlm.nih.gov/pubmed/35591246
http://dx.doi.org/10.3390/s22093557
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