@misc{c2e86d1f99ad4dd7aaf2a4abc0c683c6,
title = "Cloud-Based Artificial Intelligence System for Large-Scale Arrhythmia Screening",
abstract = "Atrial fibrillation (AFib) is the most common arrhythmia, and patients with AFib have a five times higher risk for stroke. To develop an efficient and sustainable strategy for detecting undiagnosed AFib, we propose a cloud-based artificial intelligence system for arrhythmia screening, especially for AFib.",
author = "Tseng, {Chi Ho} and Chen Lin and Chang, {Hsiang Chih} and Liu, {Cyuan Cin} and Serafico, {Bess Ma F.} and Wu, {Li Ching} and Lin, {Chih Ting} and Tien Hsu and Huang, {Chun Yao} and Lo, {Men Tzung}",
note = "Funding Information: We would like to acknowledge the Department of Public Health, Taoyuan, Taiwan, for its support in allocating possible resources for our study to ensure that mobile screening can be carried out smoothly. Research was sponsored by the Taiwan Ministry of Science and Technology (106-2917-I-564-027, 106-2221-E-008-032-MY2, 108-2221-E-008-095-MY2, and 108-2221-E-008-040-MY3). Publisher Copyright: {\textcopyright} 1970-2012 IEEE.",
year = "2019",
month = nov,
doi = "10.1109/MC.2019.2933195",
language = "English",
volume = "52",
pages = "40--51",
journal = "ACM SIGPLAN/SIGSOFT Workshop on Program Analysis for Software Tools and Engineering",
issn = "0018-9162",
publisher = "IEEE Computer Society",
}