A single-channel EEG sleep staging method based on empirical Fourier decomposition

Wen Yan Chen; Yu Liu; Jing Yuan Zhang; Jing Wei Li

doi:10.1117/12.3003854

11 October 2023 A single-channel EEG sleep staging method based on empirical Fourier decomposition

Wen Yan Chen, Yu Liu, Jing Yuan Zhang, Jing Wei Li

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128003G (2023) https://doi.org/10.1117/12.3003854
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

Automatic sleep staging can provide an aid to experts and it’s an important basis for assessing sleep quality and diagnosing sleep disorders. However, the complex non-stationary and non-linear characteristics of Electroencephalogram (EEG) signal make it difficult to use them directly for sleep staging, while existing EEG signal decomposition methods have drawbacks such as modal mixing and boundary effects. To address these issues, this paper decomposes single-channel EEG signal into sub-bands signal based on a new decomposition method, Empirical Fourier Decomposition (EFD). The time-domain, frequency-domain, and non-linear features of these sub-bands signal are then extracted. A ReliefF algorithm is then used for feature selection to obtain the feature subset with the optimal staging performance. The feature subset is input to the Random Forest (RF) to class, and the staging results of the proposed method are also compared with existing literature. The accuracy rates of the proposed algorithm in 2-6 classes of sleep staging are 97.79%, 95.35%, 94.33%, 93.35%, and 92.72%, respectively. Due to the portability of single-channel devices, the proposed method can effectively facilitate the research and application of home sleep monitoring devices.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wen Yan Chen, Yu Liu, Jing Yuan Zhang, and Jing Wei Li "A single-channel EEG sleep staging method based on empirical Fourier decomposition", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128003G (11 October 2023); https://doi.org/10.1117/12.3003854

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KEYWORDS

Electroencephalography

Tunable filters

Feature extraction

Polysomnography

Electromyography

Feature selection

Bandpass filters

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