Cardiac Electrophysiology Section, Cardiovascular Medicine Division, Department of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts 01655, USA.
Heart Rhythm. 2013 Mar;10(3):315-9. doi: 10.1016/j.hrthm.2012.12.001. Epub 2012 Dec 6.
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 diagnoseAF.
To test the hypothesis that a smartphone-based application could detect an irregular pulse fromAF.
Seventy-six 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 difference (RMSSD/mean) and 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 than in those with sinus rhythm. The 2 methods were inversely related to AF in regression models adjusting for key factors including heart rate and blood pressure (beta coefficients per SD increment in RMSDD/mean and ShE were-0.20 and-0.35; P<.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.
心房颤动（AF）很常见，与不良健康后果有关。使用传统诊断工具及时发现 AF 具有挑战性。智能手机的使用正在增加，并且可能为诊断 AF 提供一种廉价且用户友好的方法。
检验使用基于智能手机的应用程序从 AF 中检测不规则脉冲的假设。
我们同意 76 名持续性 AF 患者参与我们的研究。我们使用 iPhone 4S 摄像头在电复律前后获得脉动时间序列记录。一种新的智能手机应用程序使用两种统计方法进行实时脉搏分析：连续 RR 差值的均方根（RMSDD/mean）和香农熵（ShE）。我们使用 12 导联心电图作为金标准，检查了这两种算法的敏感性、特异性和预测准确性。
AF 患者的 RMSDD/mean 和 ShE 高于窦性心律患者。在调整了包括心率和血压在内的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 每增加一个标准差的回归系数分别为-0.20 和-0.35；P<.001）。结合这两种统计方法的算法在 AF 期间从窦性节律区分不规则脉冲的准确性、特异性和准确性方面表现出优异的性能（敏感性为 0.962，特异性为 0.975，准确性为 0.968）。
在一项前瞻性招募的 76 名因 AF 接受电复律的患者队列中，我们发现一种分析使用 iPhone 4S 记录的信号的新算法能够准确区分 AF 期间的脉搏记录与窦性节律。需要数据来探索基于智能手机的应用程序在 AF 检测中的性能和可接受性。
