Abstract
During the last years, Electrocardiographic Imaging (ECGI) has emerged as a powerful and promising clinical tool to support cardiologists. Starting from a plurality of potential measurements on the torso, ECGI yields a noninvasive estimation of their causing potentials on the epicardium. This unprecedented amount of measured cardiac signals needs to be conditioned and adapted to current knowledge and methods in cardiac electrophysiology in order to maximize its support to the clinical practice. In this setting, many cardiac indices are defined in terms of the so-called bipolar electrograms, which correspond with differential potentials between two spatially close potential measurements. Our aim was to contribute to the usefulness of ECGI recordings in the current knowledge and methods of cardiac electrophysiology. For this purpose, we first analyzed the basic stages of conventional cardiac signal processing and scrutinized the implications of the spatial-temporal nature of signals in ECGI scenarios. Specifically, the stages of baseline wander removal, low-pass filtering, and beat segmentation and synchronization were considered. We also aimed to establish a mathematical operator to provide suitable bipolar electrograms from the ECGI-estimated epicardium potentials. Results were obtained on data from an infarction patient and from a healthy subject. First, the low-frequency and high-frequency noises are shown to be non-independently distributed in the ECGI-estimated recordings due to their spatial dimension. Second, bipolar electrograms are better estimated when using the criterion of the maximum-amplitude difference between spatial neighbors, but also a temporal delay in discrete time of about 40 samples has to be included to obtain the usual morphology in clinical bipolar electrograms from catheters. We conclude that spatial-temporal digital signal processing and bipolar electrograms can pave the way towards the usefulness of ECGI recordings in the cardiological clinical practice. The companion paper is devoted to analyzing clinical indices obtained from ECGI epicardial electrograms measuring waveform variability and repolarization tissue properties.
Highlights
Despite technological advances and interpretation of arrhythmias using the skin-surface recordedElectrocardiogram (ECG), the intracardiac Electrograms (EGM) are still today the most reliable method to identify the arrhythmia mechanisms in the Electrophysiological Studies (EPS)
The information and structure of spatial-temporal autocorrelation are scrutinized both in the torso and in the epicardium signals in terms of the two preprocessing stages often used in the literature, which are baseline wander removal and the high-frequency noise filtering, and are given rationale for future development of specific preprocessing techniques overcoming the performance of current methods
Simple considerations on the electrophysiological characteristics of the estimated bipolar EGMs inspired us to propose the inclusion of a time delay on Digital Signal Processing Operator (DSPO), which make estimated EGMs and catheter-recorded EGMs in practice more consistent in terms of EGM width and non-spurius fragmentation
Summary
Despite technological advances and interpretation of arrhythmias using the skin-surface recordedElectrocardiogram (ECG), the intracardiac Electrograms (EGM) are still today the most reliable method to identify the arrhythmia mechanisms in the Electrophysiological Studies (EPS). The recording of a bipolar EGM is performed as the difference between two close electrodes, while for unipolar EGMs, either a far electrode or the average of the surface electrodes can be taken as reference This difference is amplified and filtered to remove artifacts, such as high-frequency noise, breathing, or heart movements, using high-pass filters, low-pass filters, and other well-known signal processing methods. This amplified and filtered potential difference is graphically represented for the interpretation of the electrophysiologist during the EPS [3,4]
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