{"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-deep-learning-from-raw-sensor-data","title":"End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using Wearables","arxiv_id":"1807.10707","date":"2018-07-27","proceeding":null,"authors":["Igor Gotlibovych","Stuart Crawford","Dileep Goyal","Jiaqi Liu","Yaniv Kerem","David Benaron","Defne Yilmaz","Gregory Marcus","Yihan","Li"],"abstract":"We present a convolutional-recurrent neural network architecture with long\nshort-term memory for real-time processing and classification of digital sensor\ndata. The network implicitly performs typical signal processing tasks such as\nfiltering and peak detection, and learns time-resolved embeddings of the input\nsignal. We use a prototype multi-sensor wearable device to collect over 180h of\nphotoplethysmography (PPG) data sampled at 20Hz, of which 36h are during atrial\nfibrillation (AFib). We use end-to-end learning to achieve state-of-the-art\nresults in detecting AFib from raw PPG data. For classification labels output\nevery 0.8s, we demonstrate an area under ROC curve of 0.9999, with false\npositive and false negative rates both below $2\\times 10^{-3}$. This\nconstitutes a significant improvement on previous results utilising\ndomain-specific feature engineering, such as heart rate extraction, and brings\nlarge-scale atrial fibrillation screenings within imminent reach.","url_abs":"http://arxiv.org/abs/1807.10707v1","url_pdf":"http://arxiv.org/pdf/1807.10707v1.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-deep-learning-from-raw-sensor-data","repo_url":"https://github.com/chengding0713/awesome-ppg-af-detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"atrial-fibrillation-detection","task_name":"Atrial Fibrillation Detection"},{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"photoplethysmography-ppg","task_name":"Photoplethysmography (PPG)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1807.10707","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}