TY  - JOUR
AU  - Chen, Ying-Hsien
AU  - Hung, Chi-Sheng
AU  - Huang, Ching-Chang
AU  - Hung, Yu-Chien
AU  - Hwang, Juey-Jen
AU  - Ho, Yi-Lwun
PY  - 2017
DA  - 2017/09/26
TI  - Atrial Fibrillation Screening in Nonmetropolitan Areas Using a Telehealth Surveillance System With an Embedded Cloud-Computing Algorithm: Prospective Pilot Study
JO  - JMIR Mhealth Uhealth
SP  - e135
VL  - 5
IS  - 9
KW  - atrial fibrillation
KW  - screen
KW  - cloud-computing algorithm
KW  - electrocardiography
AB  - Background: Atrial fibrillation (AF) is a common form of arrhythmia that is associated with increased risk of stroke and mortality. Detecting AF before the first complication occurs is a recognized priority. No previous studies have examined the feasibility of undertaking AF screening using a telehealth surveillance system with an embedded cloud-computing algorithm; we address this issue in this study. Objective: The objective of this study was to evaluate the feasibility of AF screening in nonmetropolitan areas using a telehealth surveillance system with an embedded cloud-computing algorithm. Methods: We conducted a prospective AF screening study in a nonmetropolitan area using a single-lead electrocardiogram (ECG) recorder. All ECG measurements were reviewed on the telehealth surveillance system and interpreted by the cloud-computing algorithm and a cardiologist. The process of AF screening was evaluated with a satisfaction questionnaire. Results: Between March 11, 2016 and August 31, 2016, 967 ECGs were recorded from 922 residents in nonmetropolitan areas. A total of 22 (2.4%, 22/922) residents with AF were identified by the physician’s ECG interpretation, and only 0.2% (2/967) of ECGs contained significant artifacts. The novel cloud-computing algorithm for AF detection had a sensitivity of 95.5% (95% CI 77.2%-99.9%) and specificity of 97.7% (95% CI 96.5%-98.5%). The overall satisfaction score for the process of AF screening was 92.1%. Conclusions: AF screening in nonmetropolitan areas using a telehealth surveillance system with an embedded cloud-computing algorithm is feasible. 
SN  - 2291-5222
UR  - https://mhealth.jmir.org/2017/9/e135/
UR  - https://doi.org/10.2196/mhealth.8290
UR  - http://www.ncbi.nlm.nih.gov/pubmed/28951384
DO  - 10.2196/mhealth.8290
ID  - info:doi/10.2196/mhealth.8290
ER  -