Fig. 4

Structure of the Bi-LSTM network, which is applied to extract 1-D morphology feature. Both Bi-LSTM layers comprised 50 hidden units, to match the 1-s-segment input. The first Bi-LSTM layer reads the data fed from the input layer and outputs a complete sequence at each time step. The second one is configured to only output at the last time step of the sequence, which can be treated as a feature vector for classification. An additional dropout layer with a probability of 0.5 is inserted between the layers to avoid overfitting, ensuring the trained model’s ability of generalization. The output feature vector from the second Bi-LSTM layer is fed into a fully connected layer, which maps the features from 50 dimensions to 2 dimensions. A Softmax function is then employed to the 2-D vector and the followed classification layer gives an AF or NAF result. The initial learning rate is set as 0.0005 and the network training takes 30 epochs