Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algor...

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Bibliographic Details
Main Authors: Mera Kartika Delimayanti, Bedy Purnama, Ngoc Giang Nguyen, Mohammad Reza Faisal, Kunti Robiatul Mahmudah, Fatma Indriani, Mamoru Kubo, Kenji Satou
Format: Article
Language:English
Published: MDPI AG 2020-03-01
Series:Applied Sciences
Subjects:
automatic sleep stage classification
electroencephalogram
fast fourier transform
Online Access:https://www.mdpi.com/2076-3417/10/5/1797

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