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

Applied Sciences

MohammadReza Faisal¹  KuntiRobiatul Mahmudah²  Fatma Indriani²  Bedy Purnama²  NgocGiang Nguyen²  MeraKartika Delimayanti²  Kenji Satou³  Mamoru Kubo³ 

[1] Computer Science, Lambung Mangkurat University, Banjarbaru 70714, Indonesia;Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa 9201192, Japan;Institute of Science and Engineering, Kanazawa University, Kanazawa 9201192, Japan;

关键词: automatic sleep stage classification;    electroencephalogram;    fast fourier transform;   

DOI  :  10.3390/app10051797

来源: DOAJ

【 摘 要 】

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 effective for the improvement of automated sleep stage classification.

【 授权许可】

Unknown