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Automatic sleep staging from single-channel electroencephalography (EEG) using artificial intelligence (AI) is emerging as an alternative to costly and time-consuming manual scoring using multi-channel polysomnography. However, current AI methods, mainly deep learning models such as convolutional neural network (CNN) and long short-term memory (LSTM), struggle to detect the N1 sleep stage, which is challenging due to its rarity and ambiguous nature compared to other stages. Here we propose SSC-SleepNet, an automatic sleep staging algorithm aimed at improving the learning of N1 sleep. SSC-SleepNet employs a pseudo-Siamese neural network architecture owing to its capability in one- or few-shot learning with contrastive loss. SSC-SleepNet consists of two branches of neural networks: a squeeze-and-excitation residual network branch and a CNN-LSTM branch. These two branches are used to generate latent features of the EEG epoch. The adaptive loss function of SSC-SleepNet uses a weighing factor to combine weighted cross-entropy loss and focal loss to specifically address the class imbalance issue inherent in sleep staging. The proposed new loss function dynamically assigns a higher penalty to misclassified N1 sleep stages, which can improve the model's learning capability for this minority class. Four datasets were used for sleep staging experiments. In the Sleep-EDF-SC, Sleep-EDF-X, Sleep Heart Health Study, and Haaglanden Medisch Centrum datasets, SSC-SleepNet achieved macro F1-scores of 84.5&#x0025;, 89.6&#x0025;, 89.5&#x0025;, and 85.4&#x0025; for all sleep stages, and N1 sleep stage F1-scores of 60.2&#x0025;, 58.3&#x0025;, 57.8&#x0025;, and 55.2&#x0025;, respectively. Our proposed deep learning model outperformed most existing models in automatic sleep staging using single-channel EEG signals. In particular, N1 detection performance has been markedly improved compared to the state-of-the-art models.

SSC-SleepNet: A Siamese-Based Automatic Sleep Staging Model With Improved N1 Sleep Detection