Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers
Accurate sleep stage classification is significant for sleep health assessment. In recent years, several machine-learning based sleep staging algorithms have been developed, and in particular, deep-learning based algorithms have achieved performance on par with human annotation. Despite improved per...
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| Main Authors: | , , , , , , |
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| Format: | Book |
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IEEE,
2024-01-01T00:00:00Z.
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| Online Access: | Connect to this object online. |
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| Summary: | Accurate sleep stage classification is significant for sleep health assessment. In recent years, several machine-learning based sleep staging algorithms have been developed, and in particular, deep-learning based algorithms have achieved performance on par with human annotation. Despite improved performance, a limitation of most deep-learning based algorithms is their black-box behavior, which have limited their use in clinical settings. Here, we propose a cross-modal transformer, which is a transformer-based method for sleep stage classification. The proposed cross-modal transformer consists of a cross-modal transformer encoder architecture along with a multi-scale one-dimensional convolutional neural network for automatic representation learning. The performance of our method is on-par with the state-of-the-art methods and eliminates the black-box behavior of deep-learning models by utilizing the interpretability aspect of the attention modules. Furthermore, our method provides considerable reductions in the number of parameters and training time compared to the state-of-the-art methods. Our code is available at <uri>https://github.com/Jathurshan0330/Cross-Modal-Transformer</uri>. A demo of our work can be found at <uri>https://bit.ly/Cross_modal_transformer_demo</uri>. |
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| Item Description: | 1534-4320 1558-0210 10.1109/TNSRE.2024.3438610 |