Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (II): Electrogram Clustering and T-Wave Alternans
Abstract
:1. Introduction
2. Methods and Materials
2.1. EGM Clustering
- Assign each observation to one cluster. For each observation () choose the index of the centroid closest to :
- Recalculate the centroid of the k-th cluster by averaging the points assigned to it.
2.2. T-Wave Alternans Algorithms
3. Experiments and Results
3.1. EGM Clustering in the Presence of Infarction
3.2. Comparison of TWA Algorithms
4. Discussion and Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Caulier-Cisterna, R.; Blanco-Velasco, M.; Goya-Esteban, R.; Muñoz-Romero, S.; Sanromán-Junquera, M.; García-Alberola, A.; Rojo-Álvarez, J.L. Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (II): Electrogram Clustering and T-Wave Alternans. Sensors 2020, 20, 3070. https://doi.org/10.3390/s20113070
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. 2020; 20(11):3070. https://doi.org/10.3390/s20113070
Chicago/Turabian StyleCaulier-Cisterna, Raúl, Manuel Blanco-Velasco, Rebeca Goya-Esteban, Sergio Muñoz-Romero, Margarita Sanromán-Junquera, Arcadi García-Alberola, and José Luis Rojo-Álvarez. 2020. "Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (II): Electrogram Clustering and T-Wave Alternans" Sensors 20, no. 11: 3070. https://doi.org/10.3390/s20113070