A Mobile Health System to Identify the Onset of Paroxysmal Atrial Fibrillation

被引：6
|
作者：
Cheng, Shi [1 ]
Tamil, Lakshman S. [1 ]
Levine, Benjamin [2 ,3 ]
机构：
[1] Univ Texas Dallas, Qual Life Technol Lab, Dallas, TX 75230 USA
[2] UT Southwestern Med Sch, Dallas, TX USA
[3] Texas Hlth Presbyterian Dallas, Dallas, TX USA
来源：
2015 IEEE INTERNATIONAL CONFERENCE ON HEALTHCARE INFORMATICS (ICHI 2015) | 2015年
关键词：
AFib; mHelath; Atrial Fibrillation; PREVALENCE; STROKE;
D O I：
10.1109/ICHI.2015.29
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Atrial Fibrillation (AFib) is increasingly recognized as a risk factor for clots, strokes, heart failure and other complications. One estimate states that 2.7 million individuals are living in U.S. with AFib and this number may increase to 5.6 million by 2050. Identifying patients with paroxysmal AFib early after the onset and treating them immediately may improve clinical outcomes, especially by reducing stroke. Currently AFib cases are identified only when the patients complain of palpitations or discovered during routine heart check ups. Improving early identification warrants a simple screening device to detect the onset of AFib. We have developed an mHealth system with a wearable ECG and an automated algorithm for this purpose. The machine learning based algorithm along with patient user interface can be downloaded as an App.
引用
收藏
页码：189 / 192
页数：4
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