心电图PR间期动态建模预测阵发性心房颤动

2009 International Conference on Biomedical and Pharmaceutical Engineering Pub Date : 2009-12-01 DOI:10.1109/ICBPE.2009.5384063

Mahnaz Arvaneh, Hamed Ahmadi, A. Azemi, M. Shajiee, Z. S. Dastgheib

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引用次数: 7

摘要

在这项工作中，我们提出了一种新的预测阵发性心房颤动(PAF)的方法，仅利用心电信号的PR间隔。首先利用遗传规划算法得到了PR区间的非线性结构和参数。接下来，我们使用神经网络对PAF进行预测。神经网络的输入是PR区间非线性模型的参数。对于建模和预测，我们将自己限制在只有30秒的心电信号，这是我们提出的方法的优点之一。为了比较，我们用基于时间的建模方法对30秒的心电信号进行了建模，并对其预测结果进行了比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Prediction of Paroxysmal Atrial Fibrillation by dynamic modeling of the PR interval of ECG

In this work, we propose a new method for prediction of Paroxysmal Atrial Fibrillation (PAF) by only using the PR interval of ECG signal. We first obtain a nonlinear structure and parameters of PR interval by a Genetic Programming (GP) based algorithm. Next, we use the neural networks for prediction of PAF. The inputs of the neural networks are the parameters of nonlinear model of the PR intervals. For the modeling and prediction we have limited ourselves to only 30 seconds of an ECG signal, which is one of the advantages of our proposed approach. For comparison purposes, we have modeled 30 seconds of ECG signals by time based modeling method and have compared prediction results of them.

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