Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals
| Autor: | Kenji Satou, Mamoru Kubo, Fatma Indriani, Kunti Robiatul Mahmudah, Ngoc Giang Nguyen, Bedy Purnama, Mohammad Reza Faisal, Mera Kartika Delimayanti |
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| Rok vydání: | 2020 |
| Předmět: |
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Computer science 0206 medical engineering Fast Fourier transform fast fourier transform Feature selection 02 engineering and technology High dimensional Electroencephalography lcsh:Technology lcsh:Chemistry 03 medical and health sciences 0302 clinical medicine medicine General Materials Science lcsh:QH301-705.5 Instrumentation automatic sleep stage classification Fluid Flow and Transfer Processes Sleep Stages medicine.diagnostic_test lcsh:T business.industry Process Chemistry and Technology General Engineering Pattern recognition electroencephalogram 020601 biomedical engineering Automation lcsh:QC1-999 Computer Science Applications lcsh:Biology (General) lcsh:QD1-999 comic_strips.comic_strip lcsh:TA1-2040 Sleep (system call) Artificial intelligence lcsh:Engineering (General). Civil engineering (General) business Brainwaves lcsh:Physics 030217 neurology & neurosurgery |
| Zdroj: | Applied Sciences, Vol 10, Iss 5, p 1797 (2020) Applied Sciences Volume 10 Issue 5 |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app10051797 |
| Popis: | 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&ndash 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. |
| Databáze: | OpenAIRE |
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