Print Email Facebook Twitter Multiway Component Analysis for the Removal of Far Ventricular Signal in Unipolar Epicardial Electrograms of Patients with Atrial Fibrillation Title Multiway Component Analysis for the Removal of Far Ventricular Signal in Unipolar Epicardial Electrograms of Patients with Atrial Fibrillation Author Maswan, Jelimo (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Microelectronics) Contributor van der Veen, A.J. (mentor) Hendriks, R.C. (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering | Circuits and Systems Date 2017-09-26 Abstract Atrial fibrillation (AF) is one of the more common clinical arrhythmias with a high morbidity and mortality. Despite this, the electrophysiological and pathological mechanisms associated with AF largely remain a mystery, encouraging the use of ever more sophisticated techniques to extract vital information for diagnostic and therapeutic purposes. Contamination by signals of ventricular origin is considered the main artifact present in high-resolution epicardial electrograms (EGMs) that hinders the accurate and efficient analysis of AF EGM datasets. Furthermore, the complexity and dynamism of AF signals calls for robust data analysis tools that can effectively reduce or remove ventricular activity (VA) while preserving the texture and morphology of atrial activity (AA). Multiway component analysis, specifically block term decomposition (BTD), proves useful for the decontamination of epicardial EGMs as demonstrated in this project by enabling the automatic estimation of VA on an electrode-by-electrode basis, which is thereafter temporally and/or power spectrally subtracted thus retaining AA at a relatively high accuracy. The performance of BTD compared to average beat subtraction (ABS) and the more restrictive canonical polyadic decomposition (CPD) is visually verified and numerically confirmed based on a set of key performance indices. Additionally, the technique is entirely data-driven i.e., does not depend on any statistical properties, but if/when available, can contribute to enhanced performance via the imposition of appropriate constraints in the tensor decomposition. Subject atrial fibrillationmultiway component analysistensor decompositionelectrogramsblock term decomposition To reference this document use: http://resolver.tudelft.nl/uuid:c1017096-f0a9-49f7-8e97-5df07d5581b5 Part of collection Student theses Document type master thesis Rights © 2017 Jelimo Maswan Files PDF JelimoMaswan_MasterThesisReport.pdf 2.49 MB Close viewer /islandora/object/uuid:c1017096-f0a9-49f7-8e97-5df07d5581b5/datastream/OBJ/view