We have recently found that our previously-developed atrial fibrillation (AF) detection algorithm for smartphones can give false positives when subjects’ fingers or hands move, as we rely on proper finger placement over the smartphone camera to collect the signal of interest. Specifically, smartphone camera pulsatile signals that are obtained from normal sinus rhythm (NSR) subjects but are corrupted by motion and noise artifacts (MNAs) are frequently detected as AF. AF and motion-corrupted episodes have the similar characteristic that pulse-to-pulse intervals are irregular. We have developed an MNA-resilient smartphone-based AF detection algorithm that first discriminates and eliminates MNA-corrupted episodes in smartphone camera recordings, and then detects AF in MNA-free recordings. We found that MNA-corrupted episodes have highly varying pulse slope, large turning point ratio, or large kurtosis values in smartphone signals compared to MNA-free AF and NSR episodes. We first use these three metrics for MNA discrimination and exclusion. Then, AF is detected in MNA-free signals using our previous algorithm. The capability to discriminate MNAs and AFs separately in smartphone signals increases the specificity of AF detection. To evaluate the performance of the proposed MNA-resilient AF algorithm, 99 subjects, including 88 study participants with AF at baseline and in NSR after electrical cardioversion as well as 11 participants with MNA-corrupted NSR, were recruited. Using iPhone 4S, 5S, and 6S models, we collected 2-min pulsatile time series from each subject. The clinical results show that the accuracy, sensitivity, and specificity of the proposed AF algorithm are 0.97, 0.98, and 0.97, respectively, which are higher than those of the previous AF algorithm.

中文翻译：

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使用智能手机的运动和噪声伪影-弹性心房颤动检测

我们最近发现，我们之前为智能手机开发的心房颤动 (AF) 检测算法可能会在受试者的手指或手移动时给出误报，因为我们依靠将手指正确放置在智能手机摄像头上来收集感兴趣的信号。具体而言，从正常窦性心律 (NSR) 对象获得但被运动和噪声伪影 (MNA) 破坏的智能手机相机脉动信号经常被检测为 AF。AF 和运动受损发作具有相似的特征，即脉搏间隔不规则。我们开发了一种基于智能手机的 MNA 弹性 AF 检测算法，该算法首先区分和消除智能手机摄像头记录中的 MNA 损坏事件，然后检测无 MNA 记录中的 AF。我们发现，与无 MNA 的 AF 和 NSR 事件相比，受 MNA 破坏的事件在智能手机信号中具有高度变化的脉冲斜率、大的转折点比或大的峰度值。我们首先将这三个指标用于 MNA 歧视和排斥。然后，使用我们之前的算法在无 MNA 的信号中检测 AF。在智能手机信号中分别区分 MNA 和 AF 的能力提高了 AF 检测的特异性。为了评估所提出的 MNA 弹性 AF 算法的性能，招募了 99 名受试者，包括 88 名基线和电复律后 NSR 的 AF 研究参与者以及 11 名 MNA 损坏的 NSR 参与者。使用 iPhone 4S、5S 和 6S 型号，我们收集了每个受试者的 2 分钟脉动时间序列。临床结果表明，准确度、灵敏度、