Abstract
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.
Highlights
Sleep is one of the basic physiological needs, and an important part of life
This study evaluated automatic sleep stage classification (AASC) methods against the sleep-EDF database based on single-channel EEG recordings, and is remarkable for having selected 10 s epochs for its analysis
In order to improve the performance of sleep stage classification, previous work has mainly focused on the following points:
Summary
Sleep is one of the basic physiological needs, and an important part of life. A typical human spends one-third of his lifetime sleeping. PSG is performed using an electronic device equipped to monitor multiple physiologic parameters during sleep by recording corresponding electrophysiological signals, for instance: from the brain via electroencephalogram (EEG), from the eyes via electrooculogram (EOG), from the skeletal muscles via electromyogram (EMG), and from the heart via electrocardiogram (ECG) [3]. To collect this data, recording devices are attached to the relevant locations of the body, typically including three EEG electrodes, one EMG electrode and two EOG electrodes. The monitoring of respiratory functions may be desired in the diagnosis of respiratory disorders such as sleep apnea and
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