Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2022
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| _version_ | 1866910901035073536 |
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| author | Pradeepkumar, Jathurshan Anandakumar, Mithunjha Kugathasan, Vinith Suntharalingham, Dhinesh Kappel, Simon L. De Silva, Anjula C. Edussooriya, Chamira U. S. |
| author_facet | Pradeepkumar, Jathurshan Anandakumar, Mithunjha Kugathasan, Vinith Suntharalingham, Dhinesh Kappel, Simon L. De Silva, Anjula C. Edussooriya, Chamira U. S. |
| contents | 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 novel cross-modal transformer encoder architecture along with a multi-scale one-dimensional convolutional neural network for automatic representation learning. Our method outperforms 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 https://github.com/Jathurshan0330/Cross-Modal-Transformer. A demo of our work can be found at https://bit.ly/Cross_modal_transformer_demo. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2208_06991 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers Pradeepkumar, Jathurshan Anandakumar, Mithunjha Kugathasan, Vinith Suntharalingham, Dhinesh Kappel, Simon L. De Silva, Anjula C. Edussooriya, Chamira U. S. Machine Learning Signal Processing 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 novel cross-modal transformer encoder architecture along with a multi-scale one-dimensional convolutional neural network for automatic representation learning. Our method outperforms 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 https://github.com/Jathurshan0330/Cross-Modal-Transformer. A demo of our work can be found at https://bit.ly/Cross_modal_transformer_demo. |
| title | Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2208.06991 |