计算机科学 ›› 2022, Vol. 49 ›› Issue (7): 57-63.doi: 10.11896/jsjkx.210800070
杨炳新, 郭艳蓉, 郝世杰, 洪日昌
YANG Bing-xin, GUO Yan-rong, HAO Shi-jie, Hong Ri-chang
摘要： 目前对抑郁症的主流诊断方式是通过医生和患者之间的沟通交流来填写相关的问卷量表,这需要相应的临床知识并且诊断结果存在主观性,给抑郁症诊断带来了很多挑战。利用信息处理技术对生理信号进行分析,构建精准客观的辅助诊断模型具有重要价值,然而目前抑郁症辅助诊断的公共数据集普遍存在样本偏少的情况,使得辅助诊断的精度普遍偏低。基于此,文中提出了一种基于数据增广和模型集成策略的图神经网络的抑郁症识别方法,该方法利用53位受试者的128通道脑电信号(Electroencephalogram,EEG),对采集到的脑电信号进行数据切分并将其用于数据增广后,利用皮尔逊相关系数计算不同通道之间的相关度,从而构造脑网络,并利用图神经网络学习脑网络的特征,然后将得到的预测结果利用模型集成策略进行多数投票,得到受试者最终的预测结果。经过实验证明,所提方法提高了网络的分类能力,解决了因样本小而带来的分类能力差的问题,在兰州大学普适感知与智能系统实验室提供的MODMA数据集(包含24名抑郁症患者和29名正常对照组)上取得了77%的分类准确率,与其他方法相比,所提方法的分类准确率有明显的提升。
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