Liu Guanzheng, Wang Lei, Wang Qian, Zhou Guangmin, Wang Ying, Jiang Qing
School of Engineering, Sun Yat-sen University, Guangzhou, China.
Shenzhen Institutes of Advanced Technology, the Chinese Academy of Sciences, Shenzhen, China.
PLoS One. 2014 Apr 18;9(4):e93399. doi: 10.1371/journal.pone.0093399. eCollection 2014.
Heart rate variability (HRV) analysis has quantified the functioning of the autonomic regulation of the heart and heart's ability to respond. However, majority of studies on HRV report several differences between patients with congestive heart failure (CHF) and healthy subjects, such as time-domain, frequency domain and nonlinear HRV measures. In the paper, we mainly presented a new approach to detect congestive heart failure (CHF) based on combination support vector machine (SVM) and three nonstandard heart rate variability (HRV) measures (e.g. SUM_TD, SUM_FD and SUM_IE). The CHF classification model was presented by using SVM classifier with the combination SUM_TD and SUM_FD. In the analysis performed, we found that the CHF classification algorithm could obtain the best performance with the CHF classification accuracy, sensitivity and specificity of 100%, 100%, 100%, respectively.
心率变异性（HRV）分析已对心脏自主调节功能及其反应能力进行了量化。然而，大多数关于HRV的研究报告了充血性心力衰竭（CHF）患者与健康受试者之间的一些差异，如时域、频域和非线性HRV测量指标。在本文中，我们主要提出了一种基于组合支持向量机（SVM）和三种非标准心率变异性（HRV）测量指标（如SUM_TD、SUM_FD和SUM_IE）来检测充血性心力衰竭（CHF）的新方法。通过使用结合SUM_TD和SUM_FD的SVM分类器建立了CHF分类模型。在进行的分析中，我们发现CHF分类算法能够获得最佳性能，CHF分类准确率、灵敏度和特异性分别为100%、100%、100%。
