{"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/end-to-end-sleep-staging-with-raw-single","title":"End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNets","arxiv_id":"1904.10255","date":"2019-04-23","proceeding":null,"authors":["Ahmed Imtiaz Humayun","Asif Shahriyar Sushmit","Taufiq Hasan","Mohammed Imamul Hassan Bhuiyan"],"abstract":"Humans approximately spend a third of their life sleeping, which makes\nmonitoring sleep an integral part of well-being. In this paper, a 34-layer deep\nresidual ConvNet architecture for end-to-end sleep staging is proposed. The\nnetwork takes raw single channel electroencephalogram (Fpz-Cz) signal as input\nand yields hypnogram annotations for each 30s segments as output. Experiments\nare carried out for two different scoring standards (5 and 6 stage\nclassification) on the expanded PhysioNet Sleep-EDF dataset, which contains\nmulti-source data from hospital and household polysomnography setups. The\nperformance of the proposed network is compared with that of the\nstate-of-the-art algorithms in patient independent validation tasks. The\nexperimental results demonstrate the superiority of the proposed network\ncompared to the best existing method, providing a relative improvement in\nepoch-wise average accuracy of 6.8% and 6.3% on the household data and\nmulti-source data, respectively. Codes are made publicly available on Github.","url_abs":"http://arxiv.org/abs/1904.10255v1","url_pdf":"http://arxiv.org/pdf/1904.10255v1.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":"end-to-end-sleep-staging-with-raw-single","repo_url":"https://github.com/mHealthBuet/ASSC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"sleep-staging","task_name":"Sleep Staging"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}