使用序列到序列深度学习方法自动睡眠阶段评分

本文链接：https://blog.youkuaiyun.com/crasher123/article/details/133370978

本文介绍了一种使用深度学习方法，特别是卷积神经网络（CNN）对单通道脑电图数据进行处理，以实现自动睡眠阶段评分的技术，包括了不同大小滤波器的CNN结构和dropout层的应用。

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深度学习方法，用于使用单通道脑电图进行自动睡眠阶段评分。

def build_firstPart_model(input_var,keep_prob_=0.5):
        # List to store the output of each CNNs
        output_conns = []

        ######### CNNs with small filter size at the first layer #########

        # Convolution
        network = tf.layers.conv1d(inputs=input_var, filters=64, kernel_size=50, strides=6,
                                 padding='same', activation=tf.nn.relu)

        network = tf.layers.max_pooling1d(inputs=network, pool_size=8, strides=8, padding='same')

        # Dropout
        network = tf.nn.dropout(network, keep_prob_)


        # Convolution
        network = tf.layers.conv1d(inputs=network, filters=128, kernel_size=8, strides=1,
                                 padding='same', activation=tf.nn.relu)

        network = tf.layers.conv1d(inputs=network, filters=128, kernel_size=8, strides=1,
                                 padding='same', activation=tf.nn.relu)
        network = tf.layers.conv1d(inputs=network, filters=128, kernel_size=8, strides=1,
                                 padding='same', activation=tf.nn.relu)


        # Max pooling
        network = tf.layers.max_pooling1d(inputs=network, pool_size=4, strides=4, padding='same')


        # Flatten
        network = flatten(name="flat1", input_var=network)


        output_conns.append(network)

        ######### CNNs with large filter size at the first layer #########



        # Convolution
        network = tf.layers.conv1d(inputs=input_var, filters=64, kernel_size=400, strides=50,
                                   padding='same', activation=tf.nn.relu)

        network = tf.layers.max_pooling1d(inputs=network, pool_size=4, strides=4, padding='same')

        # Dropout
        network = tf.nn.dropout(network, keep_prob_)

        # Convolution
        network = tf.layers.conv1d(inputs=network, filters=128, kernel_size=6, strides=1,
                                   padding='same', activation=tf.nn.relu)

        network = tf.layers.conv1d(inputs=network, filters=128, kernel_size=6, strides=1,
                                   padding='same', activation=tf.nn.relu)
        network = tf.layers.conv1d(inputs=network, filters=128, kernel_size=6, strides=1,
                                   padding='same', activation=tf.nn.relu)

        # Max pooling
        network = tf.layers.max_pooling1d(inputs=network, pool_size=2, strides=2, padding='same')

        # Flatten
        network = flatten(name="flat2", input_var=network)


        output_conns.append(network)

        # Concat
        network = tf.concat(output_conns,1, name="concat1")

        # Dropout
        network = tf.nn.dropout(network, keep_prob_)

        return network