Automatic Detection of K-Complexes Using the Cohen Class Recursiveness and Reallocation Method and Deep Neural Networks with EEG Signals

Evoked and spontaneous K-complexes are thought to be involved in sleep protection, but their role as biomarkers is still under debate. K-complexes have two major functions: first, they suppress cortical arousal in response to stimuli that the sleeping brain evaluates to avoid signaling danger; and s...

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Autores principales:	Catalin Dumitrescu, Ilona-Madalina Costea, Angel-Ciprian Cormos, Augustin Semenescu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	K-complexes sleep disorders Cohen class sleep stage classification deep neural networks Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/321ab56b92ea4e8eb6e797901dd165ca
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id	oai:doaj.org-article:321ab56b92ea4e8eb6e797901dd165ca
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spelling	oai:doaj.org-article:321ab56b92ea4e8eb6e797901dd165ca2021-11-11T19:12:16ZAutomatic Detection of K-Complexes Using the Cohen Class Recursiveness and Reallocation Method and Deep Neural Networks with EEG Signals10.3390/s212172301424-8220https://doaj.org/article/321ab56b92ea4e8eb6e797901dd165ca2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7230https://doaj.org/toc/1424-8220Evoked and spontaneous K-complexes are thought to be involved in sleep protection, but their role as biomarkers is still under debate. K-complexes have two major functions: first, they suppress cortical arousal in response to stimuli that the sleeping brain evaluates to avoid signaling danger; and second, they help strengthen memory. K-complexes also play an important role in the analysis of sleep quality, in the detection of diseases associated with sleep disorders, and as biomarkers for the detection of Alzheimer’s and Parkinson’s diseases. Detecting K-complexes is relatively difficult, as reliable methods of identifying this complex cannot be found in the literature. In this paper, we propose a new method for the automatic detection of K-complexes combining the method of recursion and reallocation of the Cohen class and the deep neural networks, obtaining a recursive strategy aimed at increasing the percentage of classification and reducing the computation time required to detect K-complexes by applying the proposed methods.Catalin DumitrescuIlona-Madalina CosteaAngel-Ciprian CormosAugustin SemenescuMDPI AGarticleK-complexessleep disordersCohen classsleep stageclassificationdeep neural networksChemical technologyTP1-1185ENSensors, Vol 21, Iss 7230, p 7230 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	K-complexes sleep disorders Cohen class sleep stage classification deep neural networks Chemical technology TP1-1185
spellingShingle	K-complexes sleep disorders Cohen class sleep stage classification deep neural networks Chemical technology TP1-1185 Catalin Dumitrescu Ilona-Madalina Costea Angel-Ciprian Cormos Augustin Semenescu Automatic Detection of K-Complexes Using the Cohen Class Recursiveness and Reallocation Method and Deep Neural Networks with EEG Signals
description	Evoked and spontaneous K-complexes are thought to be involved in sleep protection, but their role as biomarkers is still under debate. K-complexes have two major functions: first, they suppress cortical arousal in response to stimuli that the sleeping brain evaluates to avoid signaling danger; and second, they help strengthen memory. K-complexes also play an important role in the analysis of sleep quality, in the detection of diseases associated with sleep disorders, and as biomarkers for the detection of Alzheimer’s and Parkinson’s diseases. Detecting K-complexes is relatively difficult, as reliable methods of identifying this complex cannot be found in the literature. In this paper, we propose a new method for the automatic detection of K-complexes combining the method of recursion and reallocation of the Cohen class and the deep neural networks, obtaining a recursive strategy aimed at increasing the percentage of classification and reducing the computation time required to detect K-complexes by applying the proposed methods.
format	article
author	Catalin Dumitrescu Ilona-Madalina Costea Angel-Ciprian Cormos Augustin Semenescu
author_facet	Catalin Dumitrescu Ilona-Madalina Costea Angel-Ciprian Cormos Augustin Semenescu
author_sort	Catalin Dumitrescu
title	Automatic Detection of K-Complexes Using the Cohen Class Recursiveness and Reallocation Method and Deep Neural Networks with EEG Signals
title_short	Automatic Detection of K-Complexes Using the Cohen Class Recursiveness and Reallocation Method and Deep Neural Networks with EEG Signals
title_full	Automatic Detection of K-Complexes Using the Cohen Class Recursiveness and Reallocation Method and Deep Neural Networks with EEG Signals
title_fullStr	Automatic Detection of K-Complexes Using the Cohen Class Recursiveness and Reallocation Method and Deep Neural Networks with EEG Signals
title_full_unstemmed	Automatic Detection of K-Complexes Using the Cohen Class Recursiveness and Reallocation Method and Deep Neural Networks with EEG Signals
title_sort	automatic detection of k-complexes using the cohen class recursiveness and reallocation method and deep neural networks with eeg signals
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/321ab56b92ea4e8eb6e797901dd165ca
work_keys_str_mv	AT catalindumitrescu automaticdetectionofkcomplexesusingthecohenclassrecursivenessandreallocationmethodanddeepneuralnetworkswitheegsignals AT ilonamadalinacostea automaticdetectionofkcomplexesusingthecohenclassrecursivenessandreallocationmethodanddeepneuralnetworkswitheegsignals AT angelcipriancormos automaticdetectionofkcomplexesusingthecohenclassrecursivenessandreallocationmethodanddeepneuralnetworkswitheegsignals AT augustinsemenescu automaticdetectionofkcomplexesusingthecohenclassrecursivenessandreallocationmethodanddeepneuralnetworkswitheegsignals
_version_	1718431575447699456

Automatic Detection of K-Complexes Using the Cohen Class Recursiveness and Reallocation Method and Deep Neural Networks with EEG Signals

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