Lee Jinseok, Reyes Bersain A, McManus David D, Mathias Oscar, Chon Ki H
WPI, Worcester, MA 01609, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1177-80. doi: 10.1109/EMBC.2012.6346146.
We hypothesized that an iPhone 4s can be used to detect atrial fibrillation (AF) based on its ability to record a pulsatile photoplethysmogram (PPG) signal from a fingertip using the built-in camera lens. To investigate the capability of the iPhone 4s for AF detection, 25 prospective subjects with AF pre- and post-electrical cardioversion were recruited. Using an iPhone 4s, we collected 2-minute pulsatile time series. We investigated 3 statistical methods consisting of the Root Mean Square of Successive Differences (RMSSD), the Shannon entropy (ShE) and the Sample entropy (SampE), which have been shown to be useful tools for AF assessment. The beat-to-beat accuracy for RMSSD, ShE and SampE was found to be 0.9844, 0.8494 and 0.9552, respectively. It should be recognized that for clinical applications, the most relevant objective is to detect the presence of AF or normal sinus rhythm (NSR) in the data. Using this criterion, we achieved an accuracy of 100% for both detecting the presence of either AF or NSR.
我们假设，苹果iPhone 4s手机可以基于其利用内置摄像头镜头记录来自指尖的搏动性光电容积脉搏波描记图（PPG）信号的能力来检测心房颤动（AF）。为了研究iPhone 4s手机检测房颤的能力，招募了25名在电复律前后患有房颤的前瞻性受试者。我们使用iPhone 4s手机收集了2分钟的搏动性时间序列。我们研究了三种统计方法，包括逐差均方根（RMSSD）、香农熵（ShE）和样本熵（SampE），这些方法已被证明是评估房颤的有用工具。结果发现，RMSSD、ShE和SampE的逐搏准确率分别为0.9844、0.8494和0.9552。应该认识到，对于临床应用来说，最相关的目标是检测数据中是否存在房颤或正常窦性心律（NSR）。使用这一标准，我们在检测房颤或NSR的存在方面均达到了100%的准确率。
