{"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/deepsleep-2-0-automated-sleep-arousal","title":"DeepSleep 2.0: Automated Sleep Arousal Segmentation via Deep Learning","arxiv_id":null,"date":"2022-03-01","proceeding":"AI 2022 3","authors":["Robert Fonod"],"abstract":"DeepSleep 2.0 is a compact version of DeepSleep, a state-of-the-art, U-Net-inspired, fully convolutional deep neural network, which achieved the highest unofficial score in the 2018 PhysioNet Computing Challenge. The proposed network architecture has a compact encoder/decoder structure containing only 740,551 trainable parameters. The input to the network is a full-length multichannel polysomnographic recording signal. The network has been designed and optimized to efficiently predict nonapnea sleep arousals on held-out test data at a 5 ms resolution level, while not compromising the prediction accuracy. When compared to DeepSleep, the obtained experimental results in terms of gross area under the precision-recall curve (AUPRC) and gross area under the receiver operating characteristic curve (AUROC) suggest a lightweight architecture, which can achieve similar prediction performance at a lower computational cost, is realizable.","url_abs":"https://www.mdpi.com/2673-2688/3/1/10","url_pdf":"https://www.mdpi.com/2673-2688/3/1/10/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":"deepsleep-2-0-automated-sleep-arousal","repo_url":"https://github.com/rfonod/deepsleep2","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"sleep-arousal-detection","task_name":"Sleep Arousal Detection"},{"task_slug":"sleep-micro-event-detection","task_name":"Sleep Micro-event detection"},{"task_slug":"sleep-quality","task_name":"Sleep Quality"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sleep-arousal-detection-on-you-snooze-you-win","task":"Sleep Arousal Detection","dataset":"You Snooze You Win - The PhysioNet Computing in Cardiology Challenge 2018","model":"DeepSleep 2.0 - Model 2","rank_in_archive_order":2,"of":2,"metrics":{"AUPRC":"0.450434","AUROC":"0.901215"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}