A dementia classification framework using frequency and time-frequency features based on EEG signals.

Durongbhan, P., Zhao, Y., Chen, L. et al. (9 more authors) (2019) A dementia classification framework using frequency and time-frequency features based on EEG signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering. ISSN 1534-4320

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Copyright, Publisher and Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Keywords: Electroencephalogram; Alzheimer’s Disease; Machine Learning; K-Nearest Neighbour; Signal Processing; Electroencephalography; Feature extraction; Time-frequency analysis; Dementia; Biomarkers; Classification algorithms
Dates:
  • Published (online): 4 April 2019
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Sheffield Teaching Hospitals
Depositing User: Symplectic Sheffield
Date Deposited: 11 Apr 2019 12:06
Last Modified: 11 Apr 2019 12:06
Status: Published online
Publisher: IEEE
Refereed: Yes
Identification Number: https://doi.org/10.1109/TNSRE.2019.2909100
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