基于深度卷积神经网络的睡眠分期模型
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贾子钰,林友芳,张宏钧,王晶
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Sleep stage classification model based ondeep convolutional neural network
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Zi-yu JIA,You-fang LIN,Hong-jun ZHANG,Jing WANG
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表 2 预训练模型的最佳结构及详细参数 |
Tab.2 The best structure and corresponding parameters of pretrained model |
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层名称 | 层类型 | 单元数 | 激活函数 | 大小 | 步长 | Input1 | − | − | − | − | − | Con11 | Convolution | 64 | Relu | 50 | 6 | MaxP11 | MaxPooling | − | − | 8 | 8 | D11 | Dropout (0.5) | − | − | − | − | Con12 | Convolution | 128 | Relu | 8 | 1 | Con13 | Convolution | 128 | Relu | 8 | 1 | Con14 | Convolution | 128 | Relu | 8 | 1 | MaxP12 | MaxPooling | − | − | 4 | 4 | F1 | Flatten | − | − | − | − | Con21 | Convolution | 64 | Relu | 400 | 50 | MaxP21 | MaxPooling | − | − | 4 | 4 | D21 | Dropout (0.5) | − | − | − | − | Con22 | Convolution | 128 | Relu | 6 | 1 | Con23 | Convolution | 128 | Relu | 6 | 1 | Con24 | Convolution | 128 | Relu | 6 | 1 | MaxP22 | MaxPooling | − | − | 2 | 2 | F2 | Flatten | − | − | − | − | D3 | Dropout (0.5) | − | − | − | − | Dense1 | Dense | 5 | Softmax | − | − |
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