@inproceedings{7d28b0aa6e87444195bb5cb7df8f022a,
title = "Ectopic Heartbeat Detection from ECG Signals using Deep Convolutional Neural Networks",
abstract = "Electrocardiogram (ECG) signal analysis is widely used to diagnose various cardiac and non-cardiac diseases. Detecting abnormalities on ECG is critical for preventing the onset of life-threatening cardiac arrhythmias. This paper proposed a method based on deep convolutional neural network (DCNN) to detect abnormal heartbeats such as ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB). The proposed model was trained and validated on two large-sample PhysioNet's MIT-BIH datasets. A separate test result showed overall accuracy of 96% on distinguishing three types of heartbeats VEB, SVEB, and other heartbeats which are not ectopic beat (NOTEB).",
author = "Hasitha Kuruwita and {Shu Kay}, Ng and Alan Liew and Brent Richards and Kelvin Ross and Kuldeep Kumar and Luke Haseler and Meghan McConnell and Ping Zhang",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM ; Conference date: 06-12-2022 Through 08-12-2022",
year = "2022",
doi = "10.1109/BIBM55620.2022.9995477",
language = "English",
series = "Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022",
publisher = "IEEE",
pages = "3535--3540",
editor = "Donald Adjeroh and Qi Long and Xinghua Shi and Fei Guo and Xiaohua Hu and Srinivas Aluru and Giri Narasimhan and Jianxin Wang and Mingon Kang and Mondal, {Ananda M.} and Jin Liu",
booktitle = "2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
}