Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Dumitrescu, Catalin, Costea, Ilona-Madalina, Cormos, Angel-Ciprian, Semenescu, Augustin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587652/
https://www.ncbi.nlm.nih.gov/pubmed/34770537
http://dx.doi.org/10.3390/s21217230
_version_ 1784598205706534912
author Dumitrescu, Catalin
Costea, Ilona-Madalina
Cormos, Angel-Ciprian
Semenescu, Augustin
author_facet Dumitrescu, Catalin
Costea, Ilona-Madalina
Cormos, Angel-Ciprian
Semenescu, Augustin
author_sort Dumitrescu, Catalin
collection PubMed
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 Online
Article
Text
id pubmed-8587652
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-85876522021-11-13 Automatic Detection of K-Complexes Using the Cohen Class Recursiveness and Reallocation Method and Deep Neural Networks with EEG Signals Dumitrescu, Catalin Costea, Ilona-Madalina Cormos, Angel-Ciprian Semenescu, Augustin Sensors (Basel) Article 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. MDPI 2021-10-30 /pmc/articles/PMC8587652/ /pubmed/34770537 http://dx.doi.org/10.3390/s21217230 Text en © 2021 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
Dumitrescu, Catalin
Costea, Ilona-Madalina
Cormos, Angel-Ciprian
Semenescu, Augustin
Automatic Detection of K-Complexes Using the Cohen Class Recursiveness and Reallocation Method and Deep Neural Networks with EEG Signals
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587652/
https://www.ncbi.nlm.nih.gov/pubmed/34770537
http://dx.doi.org/10.3390/s21217230
work_keys_str_mv AT dumitrescucatalin automaticdetectionofkcomplexesusingthecohenclassrecursivenessandreallocationmethodanddeepneuralnetworkswitheegsignals
AT costeailonamadalina automaticdetectionofkcomplexesusingthecohenclassrecursivenessandreallocationmethodanddeepneuralnetworkswitheegsignals
AT cormosangelciprian automaticdetectionofkcomplexesusingthecohenclassrecursivenessandreallocationmethodanddeepneuralnetworkswitheegsignals
AT semenescuaugustin automaticdetectionofkcomplexesusingthecohenclassrecursivenessandreallocationmethodanddeepneuralnetworkswitheegsignals