计算机科学 ›› 2023, Vol. 50 ›› Issue (11A): 221100139-6.doi: 10.11896/jsjkx.221100139
任书瑶, 宋江玲, 张瑞
REN Shuyao, SONG Jiangling, ZHANG Rui
摘要: 抑郁症作为一类常见的、可治愈型的精神类疾病,若能在早期阶段对其进行有效筛查(即早期筛查)并及时采取相应的治疗手段,则可有效控制病情的进一步加重,甚至彻底治愈。传统的抑郁症诊断方法主要是医生通过患者的临床表现及临床检查(主要为诊断量表)进行综合判断,但诊断结果的准确与否严重依赖于医生的临床经验以及患者的高度配合。同时,由于抑郁症早期患者往往缺乏明显的病症表征,也极大增加了漏诊误诊的可能性。相关研究表明,脑电图(Electroencephalogram,EEG)能够反应受试者的精神状态,这为抑郁症的早期筛查提供了一种有效途径。基于此,以EEG信号为数据源,提出了一种基于EEG信号与深度学习的抑郁症早期筛查方法。首先,结合分段处理、频域转化等方法,对EEG信号进行时-频-空特征序列的提取;其次,基于所提特征序列与深度学习,构建了一种深度混合模型,通过训练模型完成正常人与轻度抑郁症患者的有效识别;最后,在公开数据集MODMA上验证所提方法的可行性与有效性。实验结果显示,早期筛查准确率为82.64%,召回率为78.42%,灵敏度为75.37%。
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