@classmethod
def _cnn_net(cls):
    '\n        Create the CNN net topology.\n        :return keras.Sequential(): CNN topology.\n        '
    qrs_detector = keras.Sequential()
    qrs_detector.add(keras.layers.Conv1D(96, 49, activation=tf.nn.relu, input_shape=(300, 1), strides=1, name='conv1'))
    qrs_detector.add(keras.layers.MaxPool1D(pool_size=2, strides=2, name='pool1'))
    qrs_detector.add(keras.layers.Conv1D(128, 25, activation=tf.nn.relu, strides=1, name='conv2'))
    qrs_detector.add(keras.layers.MaxPool1D(pool_size=2, strides=2, name='pool2'))
    qrs_detector.add(keras.layers.Conv1D(256, 9, activation=tf.nn.relu, strides=1, name='conv3'))
    qrs_detector.add(keras.layers.MaxPool1D(pool_size=2, strides=2, name='pool3'))
    qrs_detector.add(keras.layers.Conv1D(512, 9, activation=tf.nn.relu, strides=1, name='conv4'))
    qrs_detector.add(keras.layers.MaxPool1D(pool_size=2, strides=2, name='pool4'))
    qrs_detector.add(keras.layers.Flatten(data_format=None, name='flatten'))
    qrs_detector.add(keras.layers.Dense(units=4096, activation=tf.nn.relu, name='fc1'))
    qrs_detector.add(keras.layers.Dense(units=4096, activation=tf.nn.relu, name='fc2'))
    qrs_detector.add(keras.layers.Dropout(rate=0.5, name='drop1'))
    qrs_detector.add(keras.layers.Dense(units=2, name='classes'))
    qrs_detector.add(keras.layers.Activation(activation=tf.nn.softmax, name='softmax'))
    return qrs_detector
Create the CNN net topology.
:return keras.Sequential(): CNN topology.
@classmethod
def _cnn_net(cls):
    '\n        Create the CNN net topology.\n        :return keras.Sequential(): CNN topology.\n        '
    qrs_detector = keras.Sequential()
    qrs_detector.add(keras.layers.Conv1D(96, 49, activation=tf.nn.relu, input_shape=(300, 1), strides=1, name='conv1'))
    qrs_detector.add(keras.layers.MaxPool1D(pool_size=2, strides=2, name='pool1'))
    qrs_detector.add(keras.layers.Conv1D(128, 25, activation=tf.nn.relu, strides=1, name='conv2'))
    qrs_detector.add(keras.layers.MaxPool1D(pool_size=2, strides=2, name='pool2'))
    qrs_detector.add(keras.layers.Conv1D(256, 9, activation=tf.nn.relu, strides=1, name='conv3'))
    qrs_detector.add(keras.layers.MaxPool1D(pool_size=2, strides=2, name='pool3'))
    qrs_detector.add(keras.layers.Conv1D(512, 9, activation=tf.nn.relu, strides=1, name='conv4'))
    qrs_detector.add(keras.layers.MaxPool1D(pool_size=2, strides=2, name='pool4'))
    qrs_detector.add(keras.layers.Flatten(data_format=None, name='flatten'))
    qrs_detector.add(keras.layers.Dense(units=4096, activation=tf.nn.relu, name='fc1'))
    qrs_detector.add(keras.layers.Dense(units=4096, activation=tf.nn.relu, name='fc2'))
    qrs_detector.add(keras.layers.Dropout(rate=0.5, name='drop1'))
    qrs_detector.add(keras.layers.Dense(units=2, name='classes'))
    qrs_detector.add(keras.layers.Activation(activation=tf.nn.softmax, name='softmax'))
    return qrs_detector