Department of Electrical Engineering, Chang Gung University, and Department of Cardiology, Chang Gung Memorial Hospital, Taoyuan, Taiwan.
Ann Biomed Eng. 2010 Mar;38(3):813-23. doi: 10.1007/s10439-010-9908-6.
Distinguishing ventricular extrasystoles from normal heartbeats is crucial to cardiac arrhythmia analysis. This paper proposes novel morphological descriptors, the major portrait partition area (MPPA) and point distribution percentage (PDP), which are extracted from the reconstructed phase space of the QRS complex. These measures can be linked to QRS width and prolonged ventricular contraction, and offer several advantages over traditional characterization of the QRS structure: it does not require QRS boundary detection, is robust under R-peak misalignment, and including some information from nearby points. The first four principal components of MPPA variables and PDPs in the first and the third quadrants of the phase space diagram were used as inputs of neural networks. The performance of networks in distinguishing premature ventricular contraction events from normal heartbeats were evaluated under a series of 50 cross-validations based on the electrocardiogram data taken from the MIT/BIH arrhythmia database. The sensitivity and specificity obtained using the aforementioned MPPA principal components and PDPs as inputs were similar to those obtained using wavelet features and Hermite coefficients. However, the phase space information performed better in situations of noise contaminations and waveform deformations.
区分室性期前收缩和正常心跳对心律失常分析至关重要。本文提出了新颖的形态描述符，即主肖像分区面积（MPPA）和点分布百分比（PDP），它们是从 QRS 复合波的重建相空间中提取出来的。这些措施可以与 QRS 宽度和延长的心室收缩联系起来，并且与传统的 QRS 结构特征相比具有几个优势：它不需要 QRS 边界检测，在 R 波峰值错位时具有鲁棒性，并且包含来自附近点的一些信息。MPPA 变量的前四个主成分和相空间图第一和第三象限中的 PDP 被用作神经网络的输入。在基于麻省理工学院/比哈里心律失常数据库中获取的心电图数据的一系列 50 次交叉验证中，评估了网络在区分室性期前收缩事件和正常心跳方面的性能。使用上述 MPPA 主成分和 PDP 作为输入获得的灵敏度和特异性与使用小波特征和 Hermite 系数获得的灵敏度和特异性相似。然而，在存在噪声污染和波形变形的情况下，相空间信息表现更好。
