基于双导联ECG和多变量回归模型的远程心电诊断算法研究

An Algorithm Study on Telecardiogram Diagnosis Based on Multivariate Autoregressive Model and Two-lead ECG Signals.

目的研究适于远程心电诊断、高精度的心电信号 (ECG)直接分类方法 ,以克服接收端因信号重建而延误时间的问题。方法收集MIT BIHdatabase中正常窦性心律 (NSR)、心房早期收缩 (APC)、心室早期收缩 (PVC)、心室性心动过速 (VT)、心室纤维性颤动 (VF)和室上性心动过速 (SVT)各 30 0例进行分类研究。提出一种融合双导联ECG信号的多变量回归 (MAR)模型对心电信号直接进行分类 ,包括 :心电数据的MAR建模 ,利用MAR模型系数及其K L变换系数实施非线性二次判别函数 (QDF)法分类。结果文中方法快捷方便 ,分别获得了 98.3%至 1 0 0 %的分类精度。结论MAR建模法特别适合于远程心电诊断 。

Objective In view of the time delay caused by reconstruction of signals at remote sites, a direct classification method with high accuracy suitable for telediagnosis of electrocardiogram (ECG) signals is studied. Method The data for analysis and classification was obtained from MIT-BIH database, including 300 samples each of normal sinus rhythm (NSR), atria premature contraction (APC), premature ventricular contraction (PVC), ventricular tachycardia (VT), ventricular fibrillation (VF) and superventricular tachycardia (SVT). An multivariate autoregressine(MAR) model based technique that could combine the signals of two ECG leads was presented to classify the ECGs directly, including MAR modeling performed on ECGs, and quadratic discrimination function (QDF) based classification by using MAR coefficients and K-L MAR coefficients. Result Besides quick and convenient diagnosis, the accuracy of the proposed classification algorithm was as high as 98.3%～100%.Conclusion The MAR modeling based technique is suitable for telecardiogram diagnosis. Comparing with single-lead ECGs, better classification results can be obtained through the combination of two-lead ECG signals.