现代仪器与医疗

基于辛稀疏模态分解的抑郁症患者脑电信号眼电伪迹自适应去除方法研究

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张伟 ^1a , 涂伟 ^1b^,^* , 邓国金 ² , 戴进军 ^1c

现代仪器与医疗 | 医学工程 2026,32(2): 15-22

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现代仪器与医疗 |医学工程 2026 , 32 (2) : 15 -22

基于辛稀疏模态分解的抑郁症患者脑电信号眼电伪迹自适应去除方法研究

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张伟^1a, 涂伟^1b^,^*, 邓国金², 戴进军^1c

作者信息

^1a.神志病中医药诊疗中心，长沙　410007

^1b.湖南省第二人民医院　神经外科三病区，长沙　410007

^1c.湖南省第二人民医院　护理部，长沙　410007

^2.长沙市望城区精神病医院精神一科，长沙　410007

通讯作者:

涂伟，女，本科，主管护师，研究方向：抑郁等精神疾病护理

作者简介:

张伟，男，本科，主管护师，研究方向：无创脑刺激，脑电认知识别等

Research on an adaptive removal method for electrooculogram artifacts in electroencephalogram signals of depression patients based on symplectic sparse mode decomposition

Wei Zhang^1a, Wei Tu^1b^,^*, Guojin Deng², Jinjun Dai^1c

Affiliations

^1a.Traditional Chinese Medicine Diagnosis and Treatment Center for Mental Disorders, the Second People's Hospital of Hunan Province, Changsha 410007, China

^1b.Department 3 of Neurosurgery, the Second People's Hospital of Hunan Province, Changsha 410007, China

^1c.Nursing Department, the Second People's Hospital of Hunan Province, Changsha 410007, China

^2.Department of Psychiatry I, Wangcheng District Psychiatric Hospital, Changsha 410007, China

出版时间: 2026-04-10 doi: 10.11876/mimt202602003

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摘要

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抑郁症患者脑电信号反映了大脑内部神经元的放电活动，含有丰富的病理信息。然而，脑电微弱信号在采集中易受眨眼等生理行为造成的眼电噪声干扰。针对传统自适应信号分解方法应用于抑郁症患者脑电信号眼电伪迹去除时物理意义不足且缺乏鲁棒性的问题，提出了辛稀疏模态分解（symplectic sparsest mode decomposition，SSMD）方法。该方法首先构建辛几何原子库，并采用能量阈值评估各初始单分量的有效性。然后采用自适应滤波器对辛几何原子进行初始化自适应降噪，从而提升原子质量及算法的鲁棒性。最后以局部窄带算子为优化目标，从而使得分解结果具有更好的物理意义。仿真和实验分析结果表明，相较辛几何模态分解（symplectic geometry mode decomposition，SGMD）、变分模态分解（variational mode decomposition，VMD）和互补集合经验模态分解（complementary ensemble empirical mode decomposition，CEEMD）方法，SSMD能更准确地将抑郁症患者的有效EEG信息和眼电伪迹分离开来。抑郁症患者EEG信号采用SSMD方法降噪后的复合多尺度模糊熵（composite multi-scale fuzzy entropy，CMFE）四分位差仅为0.14，相对其他方法而言，其数据离散程度更低、聚集性更强、信号突变干扰更少。

关键词

脑电信号 / 眼电伪迹 / 辛稀疏模态分解 / 局部窄带算子 / 辛几何模态分量 / 抑郁

Abstract

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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.

Key words

Electroencephalogram signal / Electrooculography artifacts / Symplectic sparsest mode decomposition / Local narrowband operator / Symplectic geometric mode components / Depression

引用本文

张伟, 涂伟, 邓国金, 戴进军. 基于辛稀疏模态分解的抑郁症患者脑电信号眼电伪迹自适应去除方法研究. 现代仪器与医疗, 2026 , 32 (2) : 15 -22 . DOI: 10.11876/mimt202602003

Wei Zhang, Wei Tu, Guojin Deng, Jinjun Dai. Research on an adaptive removal method for electrooculogram artifacts in electroencephalogram signals of depression patients based on symplectic sparse mode decomposition[J]. Modern Instruments & Medical Treatment, 2026 , 32 (2) : 15 -22 . DOI: 10.11876/mimt202602003

基金

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湖南省自然科学基金项目(2024JJ9356)

参考文献

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2026年第32卷第2期

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文章信息

doi: 10.11876/mimt202602003

首发时间：2026-10-23
出版时间：2026-04-10

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基金

湖南省自然科学基金项目(2024JJ9356)

作者信息

^1a.神志病中医药诊疗中心，长沙　410007

^1b.湖南省第二人民医院　神经外科三病区，长沙　410007

^1c.湖南省第二人民医院　护理部，长沙　410007

^2.长沙市望城区精神病医院精神一科，长沙　410007

通讯作者:

涂伟，女，本科，主管护师，研究方向：抑郁等精神疾病护理

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2种不同金属材料的力学参数

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

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