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Caulier-Cisterna R, Blanco-Velasco M, Goya-Esteban R, Muñoz-Romero S, Sanromán-Junquera M, García-Alberola A, Rojo-Álvarez JL. Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (II): Electrogram Clustering and T-wave Alternans. SENSORS (BASEL, SWITZERLAND) 2020; 20:s20113070. [PMID: 32485879 PMCID: PMC7309062 DOI: 10.3390/s20113070] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 04/17/2020] [Accepted: 04/27/2020] [Indexed: 06/11/2023]
Abstract
During the last years, attention and controversy have been present for the first commercially available equipment being used in Electrocardiographic Imaging (ECGI), a new cardiac diagnostic tool which opens up a new field of diagnostic possibilities. Previous knowledge and criteria of cardiologists using intracardiac Electrograms (EGM) should be revisited from the newly available spatial-temporal potentials, and digital signal processing should be readapted to this new data structure. Aiming to contribute to the usefulness of ECGI recordings in the current knowledge and methods of cardiac electrophysiology, we previously presented two results: First, spatial consistency can be observed even for very basic cardiac signal processing stages (such as baseline wander and low-pass filtering); second, useful bipolar EGMs can be obtained by a digital processing operator searching for the maximum amplitude and including a time delay. In addition, this work aims to demonstrate the functionality of ECGI for cardiac electrophysiology from a twofold view, namely, through the analysis of the EGM waveforms, and by studying the ventricular repolarization properties. The former is scrutinized in terms of the clustering properties of the unipolar an bipolar EGM waveforms, in control and myocardial infarction subjects, and the latter is analyzed using the properties of T-wave alternans (TWA) in control and in Long-QT syndrome (LQTS) example subjects. Clustered regions of the EGMs were spatially consistent and congruent with the presence of infarcted tissue in unipolar EGMs, and bipolar EGMs with adequate signal processing operators hold this consistency and yielded a larger, yet moderate, number of spatial-temporal regions. TWA was not present in control compared with an LQTS subject in terms of the estimated alternans amplitude from the unipolar EGMs, however, higher spatial-temporal variation was present in LQTS torso and epicardium measurements, which was consistent through three different methods of alternans estimation. We conclude that spatial-temporal analysis of EGMs in ECGI will pave the way towards enhanced usefulness in the clinical practice, so that atomic signal processing approach should be conveniently revisited to be able to deal with the great amount of information that ECGI conveys for the clinician.
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Affiliation(s)
- Raúl Caulier-Cisterna
- Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, 28943 Fuenlabrada, Madrid, Spain; (R.C.-C.); (R.G.-E.); (S.M.-R.); (M.S.-J.)
| | - Manuel Blanco-Velasco
- Department of Signal Theory and Communications, Universidad de Alcalá, 28805 Alcalá de Henares, Madrid, Spain;
| | - Rebeca Goya-Esteban
- Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, 28943 Fuenlabrada, Madrid, Spain; (R.C.-C.); (R.G.-E.); (S.M.-R.); (M.S.-J.)
| | - Sergio Muñoz-Romero
- Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, 28943 Fuenlabrada, Madrid, Spain; (R.C.-C.); (R.G.-E.); (S.M.-R.); (M.S.-J.)
- Center for Computational Simulation, Universidad Politécnica de Madrid, 28223 Boadilla, Madrid, Spain
| | - Margarita Sanromán-Junquera
- Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, 28943 Fuenlabrada, Madrid, Spain; (R.C.-C.); (R.G.-E.); (S.M.-R.); (M.S.-J.)
| | - Arcadi García-Alberola
- Arrhythmia Unit, Hospital Clínico Universitario Virgen de la Arrixaca de Murcia, El Palmar, 30120 Murcia, Spain;
| | - José Luis Rojo-Álvarez
- Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, 28943 Fuenlabrada, Madrid, Spain; (R.C.-C.); (R.G.-E.); (S.M.-R.); (M.S.-J.)
- Center for Computational Simulation, Universidad Politécnica de Madrid, 28223 Boadilla, Madrid, Spain
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Coll-Font J, Wang L, Brooks DH. A Common-Ground Review of the Potential for Machine Learning Approaches in Electrocardiographic Imaging Based on Probabilistic Graphical Models. COMPUTING IN CARDIOLOGY 2018; 45:10.22489/CinC.2018.348. [PMID: 30899763 PMCID: PMC6424344 DOI: 10.22489/cinc.2018.348] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Machine learning (ML) methods have seen an explosion in their development and application. They are increasingly being used in many different fields with considerable success. However, although the interest is growing, their impact in the field of electrocardiographic imaging (ECGI) remains limited. One of the main reasons that ML has yet to become more prevalent in ECGI is that the published literature is scattered and there is no common ground description and comparison of these methods in an ML framework. Here we address this limitation with a review of ECGI methods from the perspective of ML. We will use probabilistic modeling to provide a common ground framework to compare different methods and well known approaches. Finally, we will discuss which approaches have been used to do inference on these models and which alternatives could be utilized as the methods in ML become more mature.
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Affiliation(s)
- Jaume Coll-Font
- Computational Radiology Lab, Children’s Hospital, Boston (MA), USA
| | - Linwei Wang
- Rochester Institute of Technology, Rochester (NY), USA
| | - Dana H Brooks
- SPIRAL Group, ECE Dept. Northeastern University, Boston (MA), USA
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