机器学习用于检测社会焦虑障碍,使用有效的连接和图形理论措施
Abdulhakim Al-Ezzi1, Nidal Kamel2, Amal A Al-Shargabi3
1Centre for Intelligent Signal & Imaging Research (CISIR), Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia.
Frontiers in psychiatry
|May 31, 2023
概括
这项研究引入了一种使用大脑信息流模式和图形理论来分类社会焦虑障碍 (SAD) 严重程度的新方法. 将部分定向连贯性 (PDC) 和图形理论结合起来,显著提高了SAD检测的准确性.
科学领域:
- 神经科学是一个神经科学.
- 医疗信息学 医疗信息学
- 计算精神病学是一种计算精神病学.
背景情况:
- 早期诊断和社会焦虑障碍 (SAD) 的分类对于有效的患者治疗和管理至关重要.
- 了解大脑信息流动模式可以帮助区分SAD严重程度.
研究的目的:
- 提出一种有效的方法来分类SAD严重程度 (严重,中度,轻度和控制),使用大脑信息流和网络拓学.
- 用机器学习分类器评估该方法的诊断性能.
主要方法:
- 在四个频段 (delta,theta,alpha,beta) 中使用部分定向连贯性 (PDC) 量化定向信息流.
- 分析了利用图形理论措施的拓网络结构.
- 使用了66名SAD患者和22名健康对照者的静止电脑电图 (EEG) 数据.
- 采用机器学习分类器,包括支持矢量机 (SVM),以区分SAD类与HC类.
主要成果:
- 在THA和α频段 (p < 0.05) 中,在SAD组和HC组之间观察到PDC的显著差异.
- 在重度和中度SAD群体中,在所有频段中,与轻度和HC群体相比,在重度和中度SAD群体中注意到了信息流的增强.
- 通过PDC和图形理论特征的结合,实现了高分类性能:92.78%的准确性,95.25%的灵敏性和94.12%的特异性使用SVM.
结论:
- 图形理论特征与PDC值的整合为识别社会焦虑障碍提供了一个有前途的工具.
- 这些发现为开发基于拓性大脑网络和机器学习的SAD诊断新生物标志物提供了潜力.
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