论文标题
描述正常节奏和心房颤动的心率变异性的统计模型
Statistical model for describing heart rate variability in normal rhythm and atrial fibrillation
论文作者
论文摘要
心率变异性（HRV）指数描述了心电图（ECG）中脱节间隔的特性。通常，HRV仅在不包括任何形式的阵发性节奏（NSR）的正常节奏（NSR）中测量。心房颤动（AF）是人口中最广泛的心律失常。通常，这种异常节奏不会被分析，也没有被认为是混乱且不可预测的。但是，患有AF的患者的HRV指数范围有所不同，但影响了它们的生理特征却很少理解。在这项研究中，我们提出了一个统计模型，该模型描述了NSR和AF中HRV指数之间的关系。该模型基于Mahalanobis距离，K-Neartible邻居方法和多元正态分布框架。使用从长期的Holter ECG中提取的NSR和AF的10分钟的NSR和AF进行验证。为了进行验证，我们在K折过程中使用了Bhattacharyya距离和Kolmogorov-Smirnov 2样品测试。该模型能够以高精度预测至少7个HRV指数。
Heart rate variability (HRV) indices describe properties of interbeat intervals in electrocardiogram (ECG). Usually HRV is measured exclusively in normal sinus rhythm (NSR) excluding any form of paroxysmal rhythm. Atrial fibrillation (AF) is the most widespread cardiac arrhythmia in human population. Usually such abnormal rhythm is not analyzed and assumed to be chaotic and unpredictable. Nonetheless, ranges of HRV indices differ between patients with AF, yet physiological characteristics which influence them are poorly understood. In this study, we propose a statistical model that describes relationship between HRV indices in NSR and AF. The model is based on Mahalanobis distance, the k-Nearest neighbour approach and multivariate normal distribution framework. Verification of the method was performed using 10 min intervals of NSR and AF that were extracted from long-term Holter ECGs. For validation we used Bhattacharyya distance and Kolmogorov-Smirnov 2-sample test in a k-fold procedure. The model is able to predict at least 7 HRV indices with high precision.
