{"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/real-time-sleep-staging-using-deep-learning","title":"Real-Time Sleep Staging using Deep Learning on a Smartphone for a Wearable EEG","arxiv_id":"1811.10111","date":"2018-11-25","proceeding":null,"authors":["Abhay Koushik","Judith Amores","Pattie Maes"],"abstract":"We present the first real-time sleep staging system that uses deep learning\nwithout the need for servers in a smartphone application for a wearable EEG. We\nemploy real-time adaptation of a single channel Electroencephalography (EEG) to\ninfer from a Time-Distributed 1-D Deep Convolutional Neural Network.\nPolysomnography (PSG)-the gold standard for sleep staging, requires a human\nscorer and is both complex and resource-intensive. Our work demonstrates an\nend-to-end on-smartphone pipeline that can infer sleep stages in just single\n30-second epochs, with an overall accuracy of 83.5% on 20-fold cross validation\nfor five-class classification of sleep stages using the open Sleep-EDF dataset.","url_abs":"http://arxiv.org/abs/1811.10111v2","url_pdf":"http://arxiv.org/pdf/1811.10111v2.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":"real-time-sleep-staging-using-deep-learning","repo_url":"https://github.com/AbhayKoushik/RealTimeSleepStaging","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"real-time-sleep-staging-using-deep-learning","repo_url":"https://github.com/kylemath/DeepEEG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"sleep-stage-detection","task_name":"Sleep Stage Detection"},{"task_slug":"sleep-staging","task_name":"Sleep Staging"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}