Full
Outline
The electroencephalogram (EEG) signals of patients with depression reflect the discharge activities of neurons within the brain and contain abundant pathological information. However, the weak EEG signals are prone to be interfered by eye-electrical noise caused by physiological behaviors such as blinking during acquisition. To address the problem that the traditional adaptive signal decomposition methods have insufficient physical meaning and lack robustness when applied to the removal of electrooculography artifacts from the EEG signals of patients with depression, the symplectic sparsest mode decomposition (SSMD) method is proposed. This method first constructs a symplectic geometric atom library and evaluates the effectiveness of each initial single component using an energy threshold. Then, an adaptive filter is used to initialize the adaptive denoising of the symplectic geometric atoms, thereby improving the atom quality and the robustness of the algorithm. Finally, with the local narrowband operator as the optimization objective, the decomposition results have better physical meaning. Simulation and experimental analysis results show that compared with the symplectic geometry mode decomposition (SGMD), variational mode decomposition (VMD), and complementary ensemble empirical mode decomposition (CEEMD) methods, SSMD can more accurately separate the effective EEG information and electrooculography artifacts of patients with depression. The quartile difference of the composite multi-scale fuzzy entropy (CMFE) of the EEG signals of patients with depression after noise reduction using the SSMD method is only 0.14, which has a lower data dispersion, stronger aggregation, and less signal mutation interference compared to other methods.
44
9
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
