A framework for cardiac arrhythmia detection from IoT-based ECGs

• Published in: World wide web (Bussum) Vol. 23; no. 5; pp. 2835 - 2850
• Main Authors: He, Jinyuan, Rong, Jia, Sun, Le, Wang, Hua, Zhang, Yanchun, Ma, Jiangang
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2020; Springer Nature B.V
• Subjects: Arrhythmia; Artificial neural networks; Cardiac arrhythmia; Classification; Computer Science; Convolution; Database Management; Engineering education; Information Systems Applications (incl.Internet); Internet of Things; Learning; Modules; Operating Systems; Physicians; Internet-of-Things; Deep learning; ECG; Cardiac arrhythmia detection; Dynamic ensemble selection; ResNet

Cardiac arrhythmia has been identified as a type of cardiovascular diseases (CVDs) that causes approximately 12 % of all deaths globally. The development of Internet-of-Things has spawned novel ways for heart monitoring but also presented new challenges for manual arrhythmia detection. An automated...