Chen S W
Department of Electronic Engineering, Chang Gung University, Tao-Yuan, Taiwan.
IEEE Trans Biomed Eng. 2000 Oct;47(10):1317-27. doi: 10.1109/10.871404.
In this paper, we describe a new approach for the discrimination among ventricular fibrillation (VF), ventricular tachycardia (VT) and superventricular tachycardia (SVT) developed using a total least squares (TLS)-based Prony modeling algorithm. Two features, dubbed energy fractional factor (EFF) and predominant frequency (PF), are both derived from the TLS-based Prony model. In general, EFF is adopted for discriminating SVT from ventricular tachyarrhythmias (i.e., VF and VT) first, and PF is then used for further separation of VF and VT. Overall classification is achieved by performing a two-stage process to the indicators defined by EFF and PF values, respectively. Tests conducted using 91 episodes drawn from the MIT-BIH database produced optimal predictive accuracy of (SVT, VF, VT) = (95.24%, 96.00%, 97.78%). A data decimation process is also introduced in the novel method to enhance the computational efficiency, resulting in a significant reduction in the time required for generating the feature values.
在本文中，我们描述了一种使用基于总体最小二乘法（TLS）的 Prony 建模算法来区分心室颤动（VF）、室性心动过速（VT）和室上性心动过速（SVT）的新方法。从基于 TLS 的 Prony 模型中导出了两个特征，分别称为能量分数因子（EFF）和主导频率（PF）。一般来说，首先采用 EFF 来区分 SVT 与室性快速心律失常（即 VF 和 VT），然后使用 PF 进一步区分 VF 和 VT。通过分别对由 EFF 和 PF 值定义的指标执行两阶段过程来实现总体分类。使用从麻省理工学院 - 贝斯以色列女执事医疗中心（MIT - BIH）数据库中提取的 91 个发作进行的测试产生了（SVT、VF、VT）=（95.24%，96.00%，97.78%）的最佳预测准确率。在该新方法中还引入了数据抽取过程以提高计算效率，从而显著减少生成特征值所需的时间。
