{"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/an-open-access-database-for-evaluating-the","title":"An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection","arxiv_id":null,"date":"2018-09-01","proceeding":"JMIHI 2018 9","authors":["Feifei Liu","Chengyu Liu","Lina Zhao","Xiangyu Zhang","Xiaoling Wu","Xiaoyan Xu","Yulin Liu","Caiyun Ma","Shoushui Wei","Zhiqiang He","Jianqing Li","Eddie Ng Yin Kwee"],"abstract":"Over the past few decades, methods for classification and detection of rhythm or morphology abnormalities in\r\nECG signals have been widely studied. However, it lacks the comprehensive performance evaluation on an open\r\ndatabase. This paper presents a detailed introduction for the database used for the 1st China Physiological\r\nSignal Challenge 2018 (CPSC 2018), which will be run as a special section during the ICBEB 2018. CPSC 2018\r\naims to encourage the development of algorithms to identify the rhythm/morphology abnormalities from 12-lead\r\nECGs. The data used in CPSC 2018 include one normal ECG type and eight abnormal types. This paper\r\ndetails the data source, recording information, patients’ clinical baseline parameters as age, gender and so\r\non. Meanwhile, it also presents the commonly used detection/classification methods for the abovementioned\r\nabnormal ECG types. We hope this paper could be a guide reference for the CPSC 2018, to facilitate the\r\nresearchers familiar with the data and the related research advances.","url_abs":"https://doi.org/10.1166/jmihi.2018.2442","url_pdf":"http://2018.icbeb.org/file/2018X_Feifei_An%20Open%20Access%20Database%20for%20Evaluating%20ECG%20abnormal%20classificaition%20algorithm.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":[],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"rhythm","task_name":"Rhythm"}],"methods":[],"datasets_introduced":[{"slug":"the-china-physiological-signal-challenge-2018","name":"The China Physiological Signal Challenge 2018","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}