计算机科学 ›› 2019, Vol. 46 ›› Issue (3): 242-247.doi: 10.11896/j.issn.1002-137X.2019.03.036
金欢欢1,尹海波2,何玲娜1
JIN Huan-huan1,YIN Hai-bo2,HE Ling-na1
摘要： 针对现阶段数据和特征决定自动睡眠分期模型的分类精度上限的问题,提出一种基于深度混合神经网络的自动睡眠分期模型。在模型主体构建方面,使用多尺度卷积神经网络自动学习高级时不变特征,使用双向门限循环单元构建的循环神经网络对时不变特征中的时间信息进行解码,并用残差连接实现时不变特征与时间信息特征的融合。在模型优化方面,将MSMOTE(Modified Synthetic Minority Oversampling Technique)重构后的数据集用于预训练,以减少类不平衡对少数类的分类效果的影响,应用Swish激活函数加速模型收敛。使用Sleep-EDF数据集中Fpz-Cz通道的原始EEG数据对模型进行15折交叉验证,得出OA(Overall Accuracy)和MF1(Macro-averaged F1-score)分别为86.85%和81.63%。提出的模型可避免特征选取的主观性以及类不平衡小数据集在深度学习中的局限性。
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