<ul><li>Accurate sleep stage classification is significant for sleep health assessment.</li><li>A new cross-modal transformer-based method for sleep stage classification is proposed.</li><li>The method outperforms the state-of-the-art methods and eliminates the black-box behavior of deep-learning models.</li><li>Considerable reductions in the number of parameters and training time are achieved compared to the state-of-the-art methods.</li></ul>

Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers

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