通过微态分析和经验模式分解提取的融合EEG特征用于诊断精神分裂症
Shirui Song1,2, Lingyan Du1,2, Jie Yin1,2
1School of Automation and Information Engineering, Sichuan University of Science and Engineering, Zigong 643000, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了一种新的计算机辅助精神分裂症诊断方法,使用脑电图 (EEG) 信号. 融合微态分析和经验模式分解 (EMD) 功能可以提高诊断准确性和症状严重程度评估.
科学领域:
- 神经科学是一个神经科学.
- 计算精神病学是一种计算精神病学.
- 生物医学工程 生物医学工程
背景情况:
- 准确的早期诊断和严重程度评估对于精神分裂症患者的治疗和康复至关重要.
- 电脑电图 (EEG) 信号提供了一个对精神疾病相关的大脑活动的非侵入性窗口.
研究的目的:
- 开发一种计算机辅助的精神分裂症诊断方法,使用EEG信号分析.
- 提高精神分裂症诊断的准确性和症状严重程度的评估.
- 通过微态分析和经验模式分解 (EMD) 来研究融合特征的实用性.
主要方法:
- 使用脑电图 (EEG) 信号来获取数据.
- 应用微态分析和经验模式分解 (EMD) 用于特征提取.
- 雇佣最小绝对收缩和选择操作员 (LASSO) 进行特征选择和物流回归进行分类.
- 使用Shapley添加式解释 (SHAP) 进行特征重要性分析.
主要成果:
- 在EMD特征有效地识别了健康对照,而微状态特征在分类症状严重程度方面表现出色.
- 融合特征在分类指标中明显优于单个EMD或微状态特征.
- 获得了高准确度: 100% (公共数据集) 和90.7% (私人数据集) 对于精神分裂症分类,以及93.6%的症状严重程度分类.
结论:
- 拟议的融合特征方法在诊断精神分裂症和评估症状严重程度方面表现出高效.
- 这种计算机辅助的方法有望改善精神分裂症的临床管理.
- 微态分析和EMD的整合为基于EEG的精神疾病分析提供了一个强大的框架.
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