BACKGROUND AND OBJECTIVE:Paroxysmal Atrial Fibrillation (PAF) is one of the most common major cardiac arrhythmia. Unless treated timely, PAF might transform into permanent Atrial Fibrillation leading to a high rate of morbidity and mortality. Therefore, increasing attention has been directed towards prediction of PAF, to enable early detection and prevent further progression of the disease. Notwithstanding the pharmacological and electrical treatments, a validated method to predict the onset of PAF is yet to be developed. We aim to address this issue through integrating classical and modern methods. METHODS:To increase the predictivity, we have made use of a combination of features extracted through linear, time-frequency, and nonlinear analyses performed on heart rate variability. We then apply a novel approach to local feature selection using meticulous methodologies, developed in our previous works, to reduce the dimensionality of the feature space. Subsequently, the Mixture of Experts classification is employed to ensure a precise decision-making on the output of different processes. In the current study, we analyzed 106 signals from 53 pairs of ECG recordings obtained from the standard database called Atrial Fibrillation Prediction Database (AFPDB). Each pair of data contains one 30-min ECG segment that ends just before the onset of PAF event and another 30-min ECG segment at least 45 min distant from the onset. RESULTS:Combining the features that are extracted using both classical and modern analyses was found to be significantly more effective in predicting the onset of PAF, compared to using either analyses independently. Also, the Mixture of Experts classification yielded more precise class discrimination than other well-known classifiers. The performance of the proposed method was evaluated using the Atrial Fibrillation Prediction Database (AFPDB) which led to sensitivity, specificity, and accuracy of 100%, 95.55%, and 98.21% respectively. CONCLUSION:Prediction of PAF has been a matter of clinical and theoretical importance. We demonstrated that utilising an optimized combination of - as opposed to being restricted to - linear, time-frequency, and nonlinear features, along with applying the Mixture of Experts, contribute greatly to an early detection of PAF, thus, the proposed method is shown to be superior to those mentioned in similar studies in the literature.

译文

背景与目的:阵发性房颤(PAF)是最常见的严重心律不齐之一。除非及时治疗,PAF可能会转变为永久性房颤,从而导致高发病率和死亡率。因此,人们越来越关注PAF的预测,以能够及早发现并预防疾病的进一步发展。尽管进行了药理学和电学治疗,但仍未开发出验证有效的预测PAF发作的方法。我们旨在通过整合古典和现代方法来解决这个问题。
方法:为了提高可预测性,我们利用了通过对心率变异性进行线性,时频和非线性分析而提取的特征的组合。然后,我们使用在我们先前的工作中开发的细致方法,将新颖的方法应用于局部特征选择,以减少特征空间的维数。随后,采用专家混合分类,以确保对不同过程的输出做出准确的决策。在当前的研究中,我们分析了53对心电图记录中的106个信号,这些记录来自称为房颤预测数据库(AFPDB)的标准数据库。每对数据包含一个30分钟的ECG段,该段在PAF事件发作之前结束,另一个30分钟的ECG段在距发作至少45分钟的距离处结束。
结果:与单独使用任何一种分析相比,结合使用经典分析和现代分析所提取的特征,在预测PAF发作方面明显更有效。而且,专家混合分类比其他知名分类器产生了更精确的类区分。使用心房颤动预测数据库(AFPDB)对所提出方法的性能进行了评估,结果数据库的敏感性,特异性和准确性分别为100%,95.55%和98.21%。
结论:PAF的预测已成为临床和理论上的重要问题。我们证明,利用-而不是限于-线性,时频和非线性特征的优化组合,以及应用专家混合,对PAF的早期检测有很大贡献,因此,该方法被展示优于文献中类似研究中提到的那些。

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