| dc.creator |
FAISAL, MOHAMMAD REZA |
|
| dc.date.accessioned |
2020-06-17T01:52:18Z |
|
| dc.date.available |
2020-06-17T01:52:18Z |
|
| dc.identifier |
http://eprints.ulm.ac.id/9524/1/applsci-10-01797-v2.pdf |
|
| dc.identifier |
FAISAL, MOHAMMAD REZA 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. |
|
| dc.identifier.uri |
https://repo-dosen.ulm.ac.id//handle/123456789/16051 |
|
| dc.description |
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 algorithms have been applied. In this paper, we demonstrate utilization of features extracted
from EEG signals via FFT to improve the performance of automated sleep stage classification through
machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT
features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF
dataset with 61 recordings, our method outperformed other state-of-the art methods. This result
indicates that high dimensional FFT features in combination with a simple feature selection is e�ective
for the improvement of automated sleep stage classification.
Keywords: automatic sleep stage classification; electroencephalogram; fast Fourier transform |
|
| dc.format |
text |
|
| dc.relation |
http://eprints.ulm.ac.id/9524/ |
|
| dc.subject |
TA Engineering (General). Civil engineering (General) |
|
| dc.title |
Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals |
|
| dc.type |
Article |
|
| dc.type |
PeerReviewed |
|