Conservation and characterisation of spatial features in a new method of data compression for body surface potential maps.

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.