{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/towards-interpretable-sleep-stage","title":"Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers","arxiv_id":"2208.06991","date":"2022-08-15","proceeding":null,"authors":["Jathurshan Pradeepkumar","Mithunjha Anandakumar","Vinith Kugathasan","Dhinesh Suntharalingham","Simon L. Kappel","Anjula C. De Silva","Chamira U. S. Edussooriya"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2208.06991v4","url_pdf":"https://arxiv.org/pdf/2208.06991v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"towards-interpretable-sleep-stage","repo_url":"https://github.com/jathurshan0330/cross-modal-transformer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"automatic-sleep-stage-classification","task_name":"Automatic Sleep Stage Classification"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multimodal-sleep-stage-detection","task_name":"Multimodal Sleep Stage Detection"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"sleep-stage-detection","task_name":"Sleep Stage Detection"},{"task_slug":"sleep-staging","task_name":"Sleep Staging"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/automatic-sleep-stage-classification-on-sleep-1","task":"Automatic Sleep Stage Classification","dataset":"Sleep-EDF","model":"Sequence Cross-Modal Transformer-15","rank_in_archive_order":2,"of":4,"metrics":{"Accuracy":"84.3","Cohen’s Kappa score":"0.785","Number of parameters (M)":"4.05"},"uses_additional_data":false},{"leaderboard":"/sota/automatic-sleep-stage-classification-on-sleep-1","task":"Automatic Sleep Stage Classification","dataset":"Sleep-EDF","model":"Epoch Cross-Modal Transformer","rank_in_archive_order":4,"of":4,"metrics":{"Accuracy":"80.8","Cohen’s Kappa score":"0.736","Number of parameters (M)":"0.32"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2208.06991","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}