Abstract
Manual staging of sleep data from EEG signals is time consuming, error prone, and resource intensive. These limitations lead to continuous interests of researchers and clinicians toward the automatic sleep stages classification of EEG signals. This paper proposes a scheme for automatic classification of sleep stages using “edge strength of visibility graph technique from single-channel EEG signals. To discover the validity of the proposed study with noise, we perform simulation analysis with two different time series named Lorenz and Rossler time series. The proposed technique accomplishes better performance than other related work for the two standard groups of sleep stages: Rechtschaffen and Kales standard and American Academy of Sleep Medicine.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Emerging Topics in Computational Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
