Sleeping stage classification based on joint quaternion valued singular spectrum analysis and ensemble empirical mode decomposition. (January 2022)

• Record Type: Journal Article
• Authors: Huang, Zuo; Ling, Bingo Wing-Kuen
• Is Part Of: Biomedical signal processing and control. Volume 71(2022)Part A
• Publication Date: 2022-01
• Subjects: Quaternion valued singular spectrum analysis -- Ensemble empirical mode decomposition -- Multi-channel electroencephalograms -- Bagging -- Sleeping stage classification; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103086
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: This paper proposes a joint quaternion valued singular spectrum analysis and ensemble empirical mode decomposition based method for performing the sleeping stage classification. The classification accuracies achieved by our proposed method are higher than those achieved by the existing methods. Abstract: Sleeping stage classification plays an important role in the diagnosis and the treatment of the sleeping related diseases. This paper proposes a joint quaternion valued singular spectrum analysis (QSSA) and ensemble empirical mode decomposition (EEMD) based method for performing the sleeping stage classification. First, the fast Fourier transform (FFT) is employed for decomposing the electroencephalograms (EEGs) into various waves. Then, both the QSSA and the EEMD are applied for denoising these waves. Finally, the features are extracted from the selected components and the bootstrap aggregating (bagging) classifier is employed for performing the sleeping stage classification. Sixteen sleeping records obtained from both the Sleep-EDF database and the Sleep-EDF expanded database in the Physio bank are employed for performing the evaluation. A statistical analysis is conducted. The computer numerical simulation results show that the accuracies achieved by our proposed algorithm for the 6 state stage classification to the 2 state stage classification based on the EEGs in the Sleep-EDF database are 89.39%, 90.66%, 94.09%, 94.17%, and 97.50%, respectively. Moreover, the classification accuracies in the Sleep-EDF expanded database are also very high. Also, the classification accuracies achieved by our proposed method are higher than those achieved by the existing methods. This demonstrates the effectiveness of our proposed method.