{"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/a-deep-learning-architecture-to-detect-events","title":"A deep learning architecture to detect events in EEG signals during sleep","arxiv_id":"1807.05981","date":"2018-07-11","proceeding":null,"authors":["Stanislas Chambon","Valentin Thorey","Pierrick J. Arnal","Emmanuel Mignot","Alexandre Gramfort"],"abstract":"Electroencephalography (EEG) during sleep is used by clinicians to evaluate\nvarious neurological disorders. In sleep medicine, it is relevant to detect\nmacro-events (> 10s) such as sleep stages, and micro-events (<2s) such as\nspindles and K-complexes. Annotations of such events require a trained sleep\nexpert, a time consuming and tedious process with a large inter-scorer\nvariability. Automatic algorithms have been developed to detect various types\nof events but these are event-specific. We propose a deep learning method that\njointly predicts locations, durations and types of events in EEG time series.\nIt relies on a convolutional neural network that builds a feature\nrepresentation from raw EEG signals. Numerical experiments demonstrate\nefficiency of this new approach on various event detection tasks compared to\ncurrent state-of-the-art, event specific, algorithms.","url_abs":"http://arxiv.org/abs/1807.05981v1","url_pdf":"http://arxiv.org/pdf/1807.05981v1.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":"a-deep-learning-architecture-to-detect-events","repo_url":"https://github.com/Dreem-Organization/dosed","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"event-detection","task_name":"Event Detection"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"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}