Chong Jo Woon, Cho Chae Ho, Tabei Fatemehsadat, Le-Anh Duy, Esa Nada, McManus David D, Chon Ki H
Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA.
Department of Medicine, Division of Cardiovascular Medicine, University of Massachusetts Medical School, MA, USA.
IEEE J Emerg Sel Top Circuits Syst. 2018 Jun;8(2):230-239. doi: 10.1109/JETCAS.2018.2818185. Epub 2018 Mar 22.
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 (PPIs) 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-minute 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, 0.97, respectively, which are higher than those of the previous AF algorithm.
我们最近发现，我们之前开发的用于智能手机的房颤（AF）检测算法在受试者手指或手部移动时可能会给出误报，因为我们依靠将手指正确放置在智能手机相机上来收集感兴趣的信号。具体而言，从正常窦性心律（NSR）受试者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房颤。房颤和运动破坏的发作具有相似的特征，即逐搏间期（PPI）不规则。我们开发了一种基于智能手机的抗MNA房颤检测算法，该算法首先在智能手机相机记录中辨别并消除MNA破坏的发作，然后在无MNA的记录中检测房颤。我们发现，与无MNA的房颤和NSR发作相比，MNA破坏的发作在智能手机信号中具有高度变化的脉冲斜率、较大的转折点比率或较大的峰度值。我们首先使用这三个指标进行MNA辨别和排除。然后，使用我们之前的算法在无MNA的信号中检测房颤。在智能手机信号中分别辨别MNA和房颤的能力提高了房颤检测的特异性。为了评估所提出的抗MNA房颤算法的性能，招募了99名受试者，包括88名基线时患有房颤且在电复律后处于NSR的研究参与者以及11名MNA破坏的NSR参与者。使用iPhone 4S、5S和6S型号，我们从每个受试者收集了2分钟的脉动时间序列。临床结果表明，所提出的房颤算法的准确性、敏感性和特异性分别为0.97、0.98、0.97，高于之前的房颤算法。
