A novel application for the detection of an irregular pulse using an iPhone 4S in patients with atrial fibrillation.
A novel application for the detection of an irregular pulse using an iPhone 4S in patients with atrial fibrillation.
复制标题
在心房颤动患者中使用iPhone 4S检测不规则脉冲的新型应用。
DOI：
10.1016/j.hrthm.2012.12.001
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发表时间：
2013-03
期刊：
影响因子：
5.5
通讯作者：
Chon, Ki H.
中科院分区：
文献类型：
--
作者：
McManus, David D.;Lee, Jinseok;Maitas, Oscar;Esa, Nada;Pidikiti, Rahul;Carlucci, Alex;Harrington, Josephine;Mick, Eric;Chon, Ki H.
心房颤动（AF）是常见的，并与不良健康结果相关。使用传统诊断工具及时检测AF可能具有挑战性。智能手机的使用越来越多，可以提供一个便宜的和用户友好的手段来诊断AF。为了测试的假设，基于智能手机的应用程序可以检测到不规则的脉冲AF。76例成人持续性AF同意参与我们的研究。我们使用iPhone 4S相机获得心脏复律前后的脉动时间序列记录。一种新型智能手机应用程序使用2种统计方法[连续RR差异的均方根（RMSSD/mean）;香农熵（ShE）]进行实时脉搏分析。我们使用12导联心电图作为金标准，检查了两种算法的灵敏度、特异性和预测准确性。与窦性心律相比，房颤患者的RMSDD/mean和ShE更高。在校正心率和血压等关键因素的回归模型中，这两种方法与AF呈负相关（RMSDD/mean和ShE中每SD增量的β系数分别为−0.20和−0.35; p<0.001）。结合2种统计方法的算法证明了AF期间不规则脉搏与窦性心律的逐搏区分的出色灵敏度（0.962）、特异性（0.975）和准确度（0.968）。在前瞻性招募的76名接受房颤复律的参与者队列中，我们发现一种分析使用iPhone 4S记录的信号的新算法可以准确区分房颤期间的脉搏记录和窦性心律。需要数据来探索基于智能手机的AF检测应用程序的性能和可接受性。
Atrial fibrillation (AF) is common and associated with adverse health outcomes. Timely detection of AF can be challenging using traditional diagnostic tools. Smartphone use is increasing and may provide an inexpensive and user-friendly means to diagnose AF. To test the hypothesis that a smartphone-based application could detect an irregular pulse from AF. 76 adults with persistent AF were consented for participation in our study. We obtained pulsatile time series recordings before and after cardioversion using an iPhone 4S camera. A novel smartphone application conducted real-time pulse analysis using 2 statistical methods [Root Mean Square of Successive RR Differences (RMSSD/mean); Shannon Entropy (ShE)]. We examined the sensitivity, specificity, and predictive accuracy of both algorithms using the 12-lead electrocardiogram as the gold standard. RMSDD/mean and ShE were higher in participants in AF compared with sinus rhythm. The 2 methods were inversely related to AF in regression models adjusting for key factors including heart rate and blood pressure (β coefficients per SD-increment in RMSDD/mean and ShE were −0.20 and −0.35; p<0.001). An algorithm combining the 2 statistical methods demonstrated excellent sensitivity (0.962), specificity (0.975), and accuracy (0.968) for beat-to-beat discrimination of an irregular pulse during AF from sinus rhythm. In a prospectively recruited cohort of 76 participants undergoing cardioversion for AF, we found that a novel algorithm analyzing signals recorded using an iPhone 4S accurately distinguished pulse recordings during AF from sinus rhythm. Data are needed to explore the performance and acceptability of smartphone-based applications for AF detection.
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