A Real-Time Atrial Fibrillation Detection Algorithm Based on the Instantaneous State of Heart Rate.
A Real-Time Atrial Fibrillation Detection Algorithm Based on the Instantaneous State of Heart Rate.
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DOI：
10.1371/journal.pone.0136544
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Zhang Y
中科院分区：
文献类型：
--
作者：
Zhou X;Ding H;Wu W;Zhang Y
心房颤动(AF)是心源性卒中最常见的原因，随着人口的老龄化，其患病率正在增加，并呈现出广泛的症状和严重程度。房颤的早期识别是预防血液凝结和中风的重要环节。在这项工作中，提出了一种实时算法，以准确地筛选心电图房颤发作。该方法采用心率序列，并涉及到符号动力学和Shannon熵的应用。利用新的递归算法，可以获得较低的计算复杂度。四组可公开访问的临床数据(长期房颤、MIT-BIH房颤、MIT-BIH心律失常和MIT-BIH正常窦律数据库)用于评估。选取第一个数据库作为训练集，绘制受试者工作特征曲线，在阈值0.639处获得最佳性能：灵敏度(Se)、特异度(Sp)、阳性预测值(Ppv)和总体准确度(Acc)分别为96.14%、95.73%、97.03%和95.97%。其他三个数据库用于独立测试。利用得到的决策阈值(即0.639)，对于第二个集合，获得的参数分别为97.37%、98.44%、97.89%和97.99%；对于第三个数据库，这些参数分别为97.83%、87.41%、47.67%和88.51%；对于第四个集合，Sp为99.68%。最新的方法也被用来进行比较。总而言之，这项研究的结果表明，符号动力学和Shannon熵的结合产生了一个有效的房颤检测器，并表明该方法在临床和非临床环境中都有实用价值。
Atrial fibrillation (AF), the most frequent cause of cardioembolic stroke, is increasing in prevalence as the population ages, and presents with a broad spectrum of symptoms and severity. The early identification of AF is an essential part for preventing the possibility of blood clotting and stroke. In this work, a real-time algorithm is proposed for accurately screening AF episodes in electrocardiograms. This method adopts heart rate sequence, and it involves the application of symbolic dynamics and Shannon entropy. Using novel recursive algorithms, a low-computational complexity can be obtained. Four publicly-accessible sets of clinical data (Long-Term AF, MIT-BIH AF, MIT-BIH Arrhythmia, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. The first database was selected as a training set; the receiver operating characteristic (ROC) curve was performed, and the best performance was achieved at the threshold of 0.639: the sensitivity (Se), specificity (Sp), positive predictive value (PPV) and overall accuracy (ACC) were 96.14%, 95.73%, 97.03% and 95.97%, respectively. The other three databases were used for independent testing. Using the obtained decision-making threshold (i.e., 0.639), for the second set, the obtained parameters were 97.37%, 98.44%, 97.89% and 97.99%, respectively; for the third database, these parameters were 97.83%, 87.41%, 47.67% and 88.51%, respectively; the Sp was 99.68% for the fourth set. The latest methods were also employed for comparison. Collectively, results presented in this study indicate that the combination of symbolic dynamics and Shannon entropy yields a potent AF detector, and suggest this method could be of practical use in both clinical and out-of-clinical settings.