{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:29:31Z","timestamp":1760236171526,"version":"build-2065373602"},"reference-count":73,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,30]],"date-time":"2021-10-30T00:00:00Z","timestamp":1635552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s and Parkinson\u2019s 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.<\/jats:p>","DOI":"10.3390\/s21217230","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T22:24:22Z","timestamp":1635805462000},"page":"7230","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Automatic Detection of K-Complexes Using the Cohen Class Recursiveness and Reallocation Method and Deep Neural Networks with EEG Signals"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3882-5062","authenticated-orcid":false,"given":"Catalin","family":"Dumitrescu","sequence":"first","affiliation":[{"name":"Department Telematics and Electronics for Transports, University \u201cPolitehnica\u201d of Bucharest, 060042 Bucharest, Romania"}]},{"given":"Ilona-Madalina","family":"Costea","sequence":"additional","affiliation":[{"name":"Department Telematics and Electronics for Transports, University \u201cPolitehnica\u201d of Bucharest, 060042 Bucharest, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7313-0952","authenticated-orcid":false,"given":"Angel-Ciprian","family":"Cormos","sequence":"additional","affiliation":[{"name":"Department Telematics and Electronics for Transports, University \u201cPolitehnica\u201d of Bucharest, 060042 Bucharest, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6864-3297","authenticated-orcid":false,"given":"Augustin","family":"Semenescu","sequence":"additional","affiliation":[{"name":"Department Engineering and Management for Transports, University \u201cPolitehnica\u201d of Bucharest, 060042 Bucharest, Romania"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/7854_2014_341","article-title":"The role of sleep in human declarative memory consolidation","volume":"25","author":"Alger","year":"2014","journal-title":"Sleep Neuronal Plast. 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