Detection of Ventricular Fibrillation by Support Vector Machine Algorithm
Detection of Ventricular Fibrillation by Support Vector Machine Algorithm
复制标题
支持向量机算法检测心室颤动
DOI：
10.1109/car.2009.29
复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Yan
中科院分区：
文献类型：
--
作者：
Qun Li;Jie Zhao;Yan
随着心脏性猝死的增多，研制一种可靠的便携式心电监护仪迫在眉睫，尤其是自动体外除颤器。AEDS中的一个关键部分是通过适当的检测算法来检测室颤(VF)。提出了多种算法，本文提出了一种基于支持向量机、Hurst指数和时延算法[相空间重构(PSR)]的算法。对于新的VF检测算法，我们计算了敏感性、特异性、阳性预测性和准确性，然后在相同条件下，使用相同的数据库和所有数据，在没有任何预选的情况下，将这些值与先前几种VF检测算法的结果进行了比较。我们使用了BIH-MIT心律失常数据库和CU数据库。实验结果表明，该算法具有较高的检测质量，且性能优于已有的其他算法。
With the increasing of sudden cardiac death, the developing of a reliable and portable electrocardiograph (ECG) monitor is imminent, especially automated external defibrillators (AEDs). A pivotal component in AEDs is the detection of ventricular fibrillation (VF) by means of appropriate detection algorithms. Various algorithms were proposed, here we proposed a new algorithm, which is based on support vector machine (SVM), Hurst index, and the time-delay algorithm [phase space reconstruction (PSR)]. For the new VF detection algorithm we calculated the sensitivity, specificity, positive predictivity and accuracy, then we compared these values with the results from an earlier investigation of several VF detection algorithms under equal conditions, using same databases and all of data without any preselection. We used the BIH-MIT arrhythmia database and the CU database. The result shows that the proposed algorithm has a high detection quality and outperforms all other investigated algorithms.