Influence of Normalization on the Analysis of Electroanatomical Maps with Manifold Harmonics

• Published in: Bioinformatics and Biomedical Engineering pp. 415 - 425
• Main Authors: Sanromán-Junquera, Margarita, Mora-Jiménez, Inmaculada, García-Alberola, Arcadio, Caamaño-Fernández, Antonio, Rojo-Álvarez, José Luis
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Cardiac navigation system; Electroanatomical maps; Electrophysiological study; Focal tachycardia location; Manifold harmonics; Normalization; Reconstruction; Regionalization
• ISBN: 3319317431; 9783319317434
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-31744-1_37

Electrical and anatomical maps (EAM) are built by cardiac navigation systems (CNS) and by Electrocardiographic Imaging systems for supporting arrhythmia ablation during electrophysiological procedures. Manifold Harmonics Analysis (MHA) has been proposed for analyzing the spectral properties of EAM of voltages and times in CNS by using a representation of the EAM supported by the anatomical mesh. MHA decomposes the EAM in a set of basis functions and coefficients which allow to conveniently reconstruct the EAM. In this work, we addressed the effect of normalization of the mesh spatial coordinates and the bioelectrical feature on the EAM decomposition for identifying regions with strong variation on the feature. For this purpose, a simulated EAM with three foci in a ventricular and in an atrial tachycardia was used. These foci were located at different distances amongst themselves, and different voltages were also considered. Our experiments show that it is possible to identify the foci origin by considering the first 3–5 projections only when normalization was considered, both for atrial and ventricular EAM. In this case, better quality in the EAM reconstruction was also obtained when using less basis functions. Hence, we conclude that normalization can help to identify regions with strong feature variation in the first stages of the EAM reconstruction.