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Motion and Noise Artifact-Resilient Atrial Fibrillation Detection Using a Smartphone

机译:使用智能手机进行运动和噪声伪影柔韧性房颤检测

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Smartphone signals corrupted by motion and noise artifacts (MNAs) are often misclassified into atrial fibrillation (AF) by our previous smartphone AF detection application. We developed an MNA-tolerant AF detection algorithm for smartphones, which first detects MNAs in the smartphone signals, removes them, and finally detects AF from the MNA-free smartphone signals. To detect MNAs, we used time and frequency-domain parameters: high-pass filtered signal amplitude, successive pulse amplitude ratio, and successive maximum dominant frequency. AFs are detected using our previous AF detection algorithm based on root mean square of successive RR difference (RMSSD) and Shannon Entropy (ShE) values. The clinical results show that the accuracy, sensitivity and specificity of the proposed AF algorithm are 0.9632, 0.9341, and 0.9899, respectively.
机译:由于运动和噪声伪影(MNA)损坏的智能手机信号经常被我们以前的智能手机AF检测应用程序误分类为房颤(AF)。我们为智能手机开发了一种耐MNA的AF检测算法,该算法首先检测智能手机信号中的MNA,然后将其删除,最后从不含MNA的智能手机信号中检测AF。为了检测MNA,我们使用了时域和频域参数:高通滤波后的信号幅度,连续脉冲幅度比和连续最大主频。使用我们以前的AF检测算法,基于连续RR差(RMSSD)和Shannon熵(ShE)值的均方根来检测AF。临床结果表明,提出的AF算法的准确性,敏感性和特异性分别为0.9632、0.9341和0.9899。

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