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深度抑郁症检测与休息状态和认知任务EEG

Dan Peng, Wei Liu, Yun Luo

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    概括

    使用电脑电图 (EEG) 的深度神经网络通过分析休息和认知任务期间的大脑模式来检测抑郁症具有前景. 这项技术提供了一种低成本,客观的方法来识别这种常见的精神障碍.

    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:

    • 抑郁症是一种普遍存在的精神障碍,影响整体健康和功能.
    • 准确和客观的抑郁症诊断目前是具有挑战性的.
    • 脑电图 (EEG) 为监测大脑活动提供了一种低成本,高性能的解决方案.

    研究的目的:

    • 通过基于EEG的神经模式,研究深度神经网络在检测抑郁症方面的有效性.
    • 分析抑郁症和没有抑郁症的个体在休息状态和认知任务期间的神经模式.
    • 通过先进的计算模型开发用于抑郁症检测的客观生物标志物.

    主要方法:

    • 采集了33名抑郁患者和40名健康对照者的EEG信号,使用可穿戴干电极.
    • 员工注意力简单图形卷积网络和变压器神经网络模型用于抑郁症检测.
    • 设计了四个实验阶段:两个休息状态和两个认知任务 (连续性能测试-相同对,Stroop彩色词测试).

    主要成果:

    • 变压器模型在认知任务上实现了0.94的曲线下面面积 (AUC) (灵敏度:0.87-0.93,特异性:0.88-0.91).
    • 变压器模型在静止状态下达到0.89的AUC (灵敏度:0.85-0.87,特异性:0.88-0.90).

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  • 在抑郁症患者中观察到神经能量下降和持续注意力和响应抑制的性能受损.

    • 结论:

    • 基于EEG的神经模式,通过深度学习模型分析,显示出客观抑郁症检测的巨大潜力.
    • 这些发现为抑郁机制和基于EEG的抑郁症生物标志物的发展提供了新的见解.
    • 由于其性能和成本效益,这种方法对临床实践和家庭使用应用都有希望.