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基于脑电图 (EEG) 分析的抑郁症检测和诊断:系统性审查

Kholoud Elnaggar¹, Mostafa M El-Gayar^1,2, Mohammed Elmogy¹

¹Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt.

Diagnostics (Basel, Switzerland)

|January 25, 2025

概括

电脑电图 (EEG) 数据与人工智能 (AI) 相结合,显示出对诊断抑郁症的前景. 本综述强调了使用EEG的AI驱动方法,以改善心理健康诊断和未来研究方向.

科学领域:

神经科学是一个神经科学.
计算机科学计算机科学
精神病学是一个精神病学.

背景情况:

精神障碍,包括抑郁症,显著影响全球公共卫生和自杀率.
脑电图 (EEG) 数据为大脑功能提供了洞察力,有助于诊断轻度抑郁症 (MDD).

研究的目的:

通过使用脑电图 (EEG) 数据,调查人工智能 (AI) 驱动的抑郁症诊断方法.
分析整合EEG与机器学习 (ML) 和深度学习 (DL) 的方法,以识别抑郁症生物标志物.

主要方法:

用ML和DL技术使用EEG信号的研究的系统分析.
对EEG预处理,特征提取和抑郁症检测模型开发的审查.
对ML和DL方法进行比较,并分析现有的抑郁症诊断数据集.

主要成果:

人工智能驱动的EEG分析提高了诊断精度,可扩展性和抑郁症检测的自动化.
确定了当前数据集的局限性,并提出了数据增强和道选择以提高准确性.
探索了未来的方向,包括转移学习,物联网集成用于持续监控,并区分抑郁症亚型.

结论:

本综述是基于EEG的抑郁症检测研究人员的参考.
提供了对未来研究方向的见解,以推进人工智能驱动的心理健康诊断.

关键词:

在EEG预处理方法.脑电图信号特征和生物标志物抑郁症诊断的ML和DL方法整合物联网和EEG的整合.轻度抑郁障碍 (MDD) 检测检测优化电脑电图 (EEG) 频道选择.

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