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Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (II): Electrogram Clustering and T-Wave Alternans
Open AccessArticle

Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (I): Preprocessing and Bipolar Potentials

1
Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, Fuenlabrada, 28943 Madrid, Spain
2
Center for Computational Simulation, Universidad Politécnica de Madrid, Boadilla, 28223 Madrid, Spain
3
Department of Signal Theory and Communications, Universidad de Alcalá, Alcalá de Henares, 28805 Madrid, Spain
4
Arrhythmia Unit, Hospital Clínico Universitario Virgen de la Arrixaca de Murcia, El Palmar, 30120 Murcia, Spain
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(11), 3131; https://doi.org/10.3390/s20113131
Received: 29 April 2020 / Revised: 28 May 2020 / Accepted: 29 May 2020 / Published: 1 June 2020
(This article belongs to the Special Issue Recent Advances in ECG Monitoring)
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. View Full-Text
Keywords: electrocardiographic imaging; signal processing; cardiac indices; spatial-temporal processing; infarction; fragmentation electrocardiographic imaging; signal processing; cardiac indices; spatial-temporal processing; infarction; fragmentation
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Caulier-Cisterna, R.; Sanromán-Junquera, M.; Muñoz-Romero, S.; Blanco-Velasco, M.; Goya-Esteban, R.; García-Alberola, A.; Rojo-Álvarez, J.L. Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (I): Preprocessing and Bipolar Potentials. Sensors 2020, 20, 3131.

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