A study on feature selection using multi-domain feature extraction for automated k-complex detection.

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Bibliographic Details
Title: A study on feature selection using multi-domain feature extraction for automated k-complex detection.
Authors: Li Y; School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, China.; Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an, Shaanxi, China.; Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an, Shaanxi, China., Dong X; School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, China., Song K; Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates., Bai X; School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, China., Li H; School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi, China., Karray F; Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates.
Source: Frontiers in neuroscience [Front Neurosci] 2023 Sep 08; Vol. 17, pp. 1224784. Date of Electronic Publication: 2023 Sep 08 (Print Publication: 2023).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Research Foundation Country of Publication: Switzerland NLM ID: 101478481 Publication Model: eCollection Cited Medium: Print ISSN: 1662-4548 (Print) Linking ISSN: 1662453X NLM ISO Abbreviation: Front Neurosci Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1662-4548
DOI:10.3389/fnins.2023.1224784