  Sleep stage classification from electroencephalogram (EEG) is significant for
the rapid evaluation of sleeping patterns and quality. A novel deep learning
architecture, ``DenseRTSleep-II'', is proposed for automatic sleep scoring from
single-channel EEG signals. The architecture utilizes the advantages of
Convolutional Neural Network (CNN), transformer network, and Bidirectional Long
Short Term Memory (BiLSTM) for effective sleep scoring. Moreover, with the
addition of a weighted multi-loss scheme, this model is trained more implicitly
for vigorous decision-making tasks. Thus, the model generates the most
efficient result in the SleepEDFx dataset and outperforms different
state-of-the-art (IIT-Net, DeepSleepNet) techniques by a large margin in terms
of accuracy, precision, and F1-score.
