Hayn Dieter, Kollmann Alexander, Schreier Günter
Austrian Research Centers GmbH - ARC - eHealth Systems, Graz, Austria.
Biomed Tech (Berl). 2007 Feb;52(1):5-10. doi: 10.1515/BMT.2007.003.
Atrial fibrillation is the most common cardiac arrhythmia, affecting more than two million people in the US. Several therapies for patients with atrial fibrillation are available, but methods to help physicians select the optimal therapy for an individual patient are still required. Knowledge of whether a patient with a normal ECG will exhibit atrial fibrillation in the future, as well as whether atrial fibrillation will terminate spontaneously, would be very useful in clinical routine. The paper presents a software system for predicting the initiation and termination of atrial fibrillation from the ECG. The algorithms have been validated on ECGs from several signal databases. Prediction of the initiation of atrial fibrillation was achieved by detecting premature heart beats and analyzing the morphology of their P waves. Prediction of the termination of atrial fibrillation was based on calculation of the major atrial frequency. This frequency has been shown to decrease significantly prior to the termination of atrial fibrillation. Nevertheless, the effect is much less distinct in the large data set used for this study compared to previous studies. The initiation of atrial fibrillation, however, could be correctly predicted in approximately 75% of the data analyzed.
心房颤动是最常见的心律失常，在美国影响着超过200万人。目前有几种针对心房颤动患者的治疗方法，但仍需要帮助医生为个体患者选择最佳治疗方案的方法。了解心电图正常的患者未来是否会出现心房颤动，以及心房颤动是否会自发终止，在临床实践中会非常有用。本文介绍了一种用于从心电图预测心房颤动起始和终止的软件系统。这些算法已在多个信号数据库的心电图上得到验证。通过检测早搏并分析其P波形态来实现心房颤动起始的预测。心房颤动终止的预测基于主要心房频率的计算。研究表明，在心房颤动终止前，该频率会显著下降。然而，与之前的研究相比，在本研究使用的大数据集中，这种效应不太明显。不过，在大约75%的分析数据中，可以正确预测心房颤动的起始。
