基于相空间分析的心律失常检测

通过心电信号分析区分心脏的不同病态,对自动体外除颤器等治疗心律失常的设备至关重要.基于相空间分析可以有效地检测心律失常:先重构心电信号的相空间;然后从几何和信息论的角度计算重构相空间中点的密度分布熵,作为下一步分类的特征;最后用以马氏距离为度量标准的最近邻法对窦性、室上速、房扑和房颤信号进行分类.为评价本算法检测心律失常的敏感性、特异性和准确率,分别对MIT-BIH的arrhythmias数据库和犬心外膜数据库进行实验研究.结果表明:本方法计算简单,能快速准确地检测窦性、室上速、房扑和房颤信号,有望应用于治疗心律失常的自动装置.

Detecting Cardiac Arrhythmias Based on Phase Space Analysis

It is important for cardiac therapy devices such as the automated external defibrillator to discriminate different cardiac disorders based on Electrocardiogram analysis. A phase space analysis based algorithm is proposed to detect cardiac arrhythmias effectively. Firstly, the phase space of the signal is reconstructed. Then from the viewpoint of geometry and information theory, the distribution entropy of the point density in the two-dimensional reconstructed phase space is calculated as the features in the further classification. Finally the nearest-neighbour method based on Mahalanobis distance is used to classify the sinus rhythm (SR), supraventricular tachyarrhythmia (SVTA), atrial flutter (AFL) and atrial fibrillation (AF). To evaluate the sensitivity, specificity and accuracy of this proposed method in the cardiac arrhythmias classification, the MIT-BIH arrhythmias database and the canine endocardial database are studied respectively. Experiment results demonstrate that the proposed method can detect SR, SVTA, AFL and AF signals rapidly and accurately with the simple computation. It promises to find application in automated devices for cardiac arrhythmias therapy.