Atrial Fibrillation Detection Using an iPhone 4S
Atrial Fibrillation Detection Using an iPhone 4S
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DOI：
10.1109/tbme.2012.2208112
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发表时间：
2013-01-01
影响因子：
4.6
通讯作者：
Chon, Ki H.
中科院分区：
文献类型：
--
作者：
Lee, Jinseok;Reyes, Bersain A.;Chon, Ki H.
房颤(AF)影响着300至500万美国人，并与显著的发病率和死亡率有关。诊断这种阵发性心律失常的现有方法繁琐和/或昂贵。我们假设，iPhone4S可以用来检测房颤，因为它使用内置的相机镜头从指尖记录脉动的光体积图信号。为了研究iPhone 4S检测房颤的能力，我们首先使用了两个数据库，MIT-BIH房颤和正常窦性心律(NSR)，得出了两种节律之间的区分阈值。这两个数据库都包括源自250赫兹采样心电记录的RR时间序列。我们将RR时间序列重新调整为30赫兹，使RR时间序列的分辨率为1/30(S)，相当于iPhone4S的分辨率。我们研究了三种统计方法，即连续差值均方根(RMSSD)、香农熵(SHE)和样本熵(SAMPE)，这三种方法已被证明是评估房颤的有用工具。使用麻省理工学院-北京卫生研究院数据库中的节拍片段，我们发现RMSSD、SHE和SAMPE的节拍准确率值分别为0.9405、0.9300和0.9614。使用iPhone4S，我们收集了25名房颤患者在电复律前和电复律后2分钟的脉搏时间序列。使用从麻省理工学院BIH数据库中导出的RMSSD、SHE和SAMPE的阈值，我们发现节拍准确率分别为0.9844、0.8494和0.9522。应该认识到，对于临床应用，最相关的目标是检测数据中是否存在房颤。使用这个标准，我们在MIT-BIH AF和iPhone 4S数据库上都达到了100%的准确率。
Atrial fibrillation (AF) affects three to five million Americans and is associated with significant morbidity and mortality. Existing methods to diagnose this paroxysmal arrhythmia are cumbersome and/or expensive. We hypothesized that an iPhone 4S can be used to detect AF based on its ability to record a pulsatile photoplethysmogram signal from a fingertip using the built-in camera lens. To investigate the capability of the iPhone 4S for AF detection, we first used two databases, the MIT-BIH AF and normal sinus rhythm (NSR) to derive discriminatory threshold values between two rhythms. Both databases include RR time series originating from 250 Hz sampled ECG recordings. We rescaled the RR time series to 30 Hz so that the RR time series resolution is 1/30 (s) which is equivalent to the resolution from an iPhone 4S. We investigated three statistical methods consisting of the root mean square of successive differences (RMSSD), the Shannon entropy (ShE) and the sample entropy (SampE), which have been proved to be useful tools for AF assessment. Using 64-beat segments from the MIT-BIH databases, we found the beat-to-beat accuracy value of 0.9405, 0.9300, and 0.9614 for RMSSD, ShE, and SampE, respectively. Using an iPhone 4S, we collected 2-min pulsatile time series from 25 prospectively recruited subjects with AF pre- and postelectrical cardioversion. Using derived threshold values of RMSSD, ShE and SampE from the MIT-BIH databases, we found the beat-to-beat accuracy of 0.9844, 0.8494, and 0.9522, respectively. It should be recognized that for clinical applications, the most relevant objective is to detect the presence of AF in the data. Using this criterion, we achieved an accuracy of 100% for both the MIT-BIH AF and iPhone 4S databases.