Gilat S, Adam D
Julius Silver Institute of Biomedical Engineering, Department of Biomedical Engineering, Haifa, Israel.
Med Biol Eng Comput. 1992 Jan;30(1):15-25. doi: 10.1007/BF02446188.
Body surface potential maps consist of a huge amount of data represented as a series of three-dimensional maps, which are time consuming to process and expensive to store. In spite of the continuous interest in body surface potential maps, their use has not become common and they are of no practical use in the clinics. This is due to the overwhelming amount of measured data required to generate the maps and the lack of quantitative methods to analyse them. Data compression or reduction may solve these deficiencies. Such a procedure must conserve the fine spatial details of the maps, which are usually extracted from low level surface potentials, as these are reported to be significant in diagnostic electrocardiography. A technique is presented for data reduction, that implements two-level thresholding and conserves the fine significant spatial features of each map. A sequence of annuli thus produced is shown to describe the dynamic nature of the underlying process. This sequence is further processed and characterised by features which quantify its dynamic behaviour: time of annuli sequence appearance, its duration, three-dimensional loci of centres of mass of the annuli, distances between successive centres of mass and cross-correlation coefficients between successive annuli. To test the data reduction procedure and the usefulness of the features, maps from 20 subjects are studied (both normal patients and those with various pathologies). It is found that the use of annuli instead of the whole measured information allows simple storage, display and calculations; the features, which vary in time, represent closely the changes in location of the annuli and their dynamic variations of shape. The features are also found to be grouped together for the maps of the normal patients and for each pathology. Thus, body surface potential maps may become more commonly used in clinics by being represented by a set of features, which conserve their dynamic and spatial nature, and which may serve for classification of cardiac pathologies.
体表电位图由大量数据组成，这些数据以一系列三维地图的形式呈现，处理起来耗时且存储成本高昂。尽管人们对体表电位图一直很感兴趣，但它们的应用并不普遍，在临床上也没有实际用途。这是由于生成这些地图需要大量的测量数据，并且缺乏分析它们的定量方法。数据压缩或精简可能会解决这些不足。这样的过程必须保留地图的精细空间细节，这些细节通常从低水平的表面电位中提取，因为据报道这些在诊断心电图中很重要。本文提出了一种数据精简技术，该技术实现了两级阈值处理，并保留了每个地图的精细重要空间特征。由此产生的一系列环形区域被证明可以描述潜在过程的动态性质。这个序列经过进一步处理，并通过量化其动态行为的特征来表征：环形区域序列出现的时间、持续时间、环形区域质心的三维位置、连续质心之间的距离以及连续环形区域之间的互相关系数。为了测试数据精简过程和这些特征的有用性，对20名受试者（包括正常患者和患有各种疾病的患者）的地图进行了研究。结果发现，使用环形区域而不是整个测量信息可以实现简单的存储、显示和计算；随时间变化的特征紧密地代表了环形区域位置的变化及其形状的动态变化。还发现这些特征在正常患者的地图和每种疾病的地图中是聚集在一起的。因此，体表电位图通过用一组保留其动态和空间性质且可用于心脏疾病分类的特征来表示，可能会在临床上更普遍地使用。
