Department of Electronic Engineering, Fudan University, 220 Handan Road, Shanghai 200433, China.
Med Eng Phys. 2013 May;35(5):668-75. doi: 10.1016/j.medengphy.2012.07.012. Epub 2012 Aug 24.
The purpose of this study is to predict atrial fibrillation (AF) from epicardial signals by investigating the recurrence property of atrial activity dynamic system before AF. A novel scheme is proposed to predict AF by using multi-threshold spectra of the recurrence complex network. Firstly, epicardial signals are transformed into the recurrence complex network to quantify structural properties of the recurrence in the phase space. Spectral parameters with multi-threshold are used to characterize the global structure of the network. Then the feature sequential forward searching algorithm and mutual information based Maximum Relevance Minimum Redundancy criterion are used to find the optimal feature set. Finally, a support vector machine is used to predict the occurrence of AF. This method is assessed on the pre-AF epicardial signals of canine which includes the normal group A (no further AF will happen), the mild group B (the following AF time is less than 180s) and the severe group C (the following AF time is more than 180s). 25 optimal features are selected out of 180 features from each sample. With these features, sensitivity, specificity and accuracy are 99.40%, 99.70% and 99.60%, respectively, which are the best among the recurrence based methods. The results suggest that the proposed method can predict AF accurately and thus can be prospectively used in the postoperative evaluation.
本研究旨在通过研究心房活动动力系统在房颤（AF）发生前的复发特性，从心外膜信号预测房颤。提出了一种新的方案，通过使用复发性复杂网络的多阈值谱来预测房颤。首先，将心外膜信号转换为复发性复杂网络，以量化相空间中复发的结构特性。使用多阈值的谱参数来描述网络的整体结构。然后，使用特征顺序前向搜索算法和基于互信息的最大相关性最小冗余准则来寻找最优特征集。最后，使用支持向量机来预测 AF 的发生。该方法在犬的预 AF 心外膜信号上进行了评估，包括正常组 A（不会再发生 AF）、轻度组 B（后续 AF 时间小于 180s）和重度组 C（后续 AF 时间大于 180s）。从每个样本中选择了 25 个最优特征。利用这些特征，敏感性、特异性和准确性分别为 99.40%、99.70%和 99.60%，在基于复发的方法中表现最佳。结果表明，该方法可以准确预测 AF，因此可以在术后评估中得到预期的应用。
