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Bioelectric model of atrial fibrillation: applicability of blind source separation techniques for atrial activity estimation in atrial fibrillation episodes

机译:心房颤动的生物电模型:盲源分离技术在房颤发作中心房活动估计中的适用性

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In this contribution, we present the theoretical justification that give support to the suitability of blind signal separation (BSS) techniques for the estimation of the atrial activity (AA) present in ECGs of persistent atrial fibrillation (AF). The application of BSS methods to this problem needs the fulfillment of several conditions regarding AA, ventricular activity (VA) and the fashion in which both activities arise on the body surface, that will be justified along the paper. To empirically validate the model, an ICA method is applied to 10 real 12-lead recordings of AF. The identification of AA is put forward based on kurtosis and spectral analysis. The kurtosis value of the estimated AA was always below zero (k/sub AA/ = -0.36 /spl plusmn/ 0.14), while for the VA was above 20 (k/sub VA/ = 30.44 /spl plusmn/ 7.83). As conclusion, the verification of the AF bioelectric model makes feasible the application of BSS, and this contribution has indeed justified, for the first time, the theoretical background that supports the applicability of these techniques and empirically its usefulness to solve the AA extraction problem.
机译:在这项贡献中,我们提出了理论依据,为盲信号分离(BSS)技术对持续性心房颤动(AF)ECG中存在的心房活动(AA)评估的适用性提供了支持。 BSS方法在此问题上的应用需要满足以下几个条件:AA,心室活动(VA)以及两种活动都在体表上出现的方式,这在本文中是合理的。为了凭经验验证模型,将ICA方法应用于10条真实的12导联AF记录。基于峰度和光谱分析提出了氨基酸的鉴别方法。估计的AA的峰度值始终低于零(k / sub AA / = -0.36 / spl plusmn / 0.14),而VA则高于20(k / sub VA / = 30.44 / spl plusmn / 7.83)。总之,对AF生物电模型的验证使BSS的应用成为可能,而这一贡献确实首次证明了支持这些技术的适用性的理论背景,并从经验上证明了其解决AA提取问题的实用性。

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