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使用浅层和深度学习技术在基于EEG检测主要抑郁症的进展:系统性审查

Hoorain Rehman¹, Danish M Khan², Hafsa Amanullah³

¹Department of Telecommunications Engineering, NED University of Engineering and Technology, Karachi, Pakistan.

Computers in biology and medicine

|April 24, 2025

概括

人工智能 (AI) 和脑电图 (EEG) 显示出对诊断严重抑郁症 (MDD) 的前景. 本次审查强调了EEG标志物作为MDD检测的潜在客观生物标志物.

科学领域:

神经科学是一个神经科学.
人工智能的人工智能
精神病学是一个精神病学.

背景情况:

目前的大型抑郁症 (MDD) 诊断依赖于主观方法,导致不一致.
需要客观的诊断标准来进行早期干预和精确的评估.
人工智能 (AI) 分析的脑电图 (EEG) 功能提供了一个潜在的客观方法.

研究的目的:

系统地审查使用人工智能的基于EEG检测MDD的进展.
探索神经机制并确定MDD诊断的潜在生物标志物.
评估浅层和深度学习方法在MDD的EEG分析中的有用性.

主要方法:

按照PRISMA指南进行系统审查.
在Scopus,IEEE Xplore和ScienceDirect数据库中进行了搜索.
包括22项相关研究分析了EEG标志物,如频段功率,不对称性,ERP和连接性.

主要成果:

脑电图标记,包括频段功率,不对称性,ERP组件和连接度量,有效地将MDD患者与健康对照区分开来.
浅层和深度学习方法都在分析EEG数据以检测MDD时具有实用性.
关键的EEG标志物显示出作为MDD的客观生物标志物的潜力.

结论:

脑电图分析,特别是人工智能分析,显示出客观MDD诊断的巨大潜力.
需要进一步的研究来提高MDD中的EEG指标的可解释性.
未来的方向包括改进人工智能模型和验证EEG生物标志物的临床应用.

关键词:

深度学习是一种深度学习.诊断诊断诊断诊断诊断电脑电图 (EEG) 是一种电脑电图.机器学习是机器学习.大型抑郁症主要是抑郁症.

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