The area of a unipolar electrogram to identify the arrhythmogenic substrate

Master thesis (2021)

Authors

E. van Twist Mechanical Engineering

Contributors

N.M.S. de Groot Signal Processing Systems - EEMCS (mentor)
R.C. Hendriks Signal Processing Systems - EEMCS (graduation committee member)
Mathijs S. van Schie Erasmus MC (coach)
Faculty
Mechanical Engineering, Mechanical Engineering
Copyright: © 2021 Eris van Twist
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Published Date

11-10-2021

Language

English

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Faculty

Mechanical Engineering

Abstract

Background: Unipolar electrograms (U-EGMs) contain additional information about interatrial activation and conduction in their morphology, which may aid towards improved diagnosis and staging of atrial fibrillation (AF).Objective: The primary objective is to investigate regional differences in electrogram area (EA) during SR and AF and to design a patient-specific EA fingerprint, to characterize the arrhythmogenic substrate in patients with mitral valve disease (MVD).Methods: Patients (N = 42) either with (‘AF group’, N = 23) or without a history of AF (‘No AF group’, N = 19), undergoing elective open heart surgery underwent high-resolution mapping of the right atrium (RA), left atrium (LA) and pulmonary veins (PV) including Bachmann’s bundle (BB). Spatial distributions of mean EA, variance and total EA were determined in SR and AF. Absolute EA values were correlated with amplitude, an established metric in substrate mapping.Results: A total of 3104460 EAs were analysed and compared between rhythms, regions and groups (Table 3). EA was larger in AF [SR: 54.97 (42.87), AF: 57.03 (51.02), p < 0.01], but smaller per region except the RA. In patients with AF, EA was significantly smaller across all atrial regions. During AF, amplitude showed moderate correlation with EA at best [no AF: r = 0.54 vs. AF: = 0.51].Conclusion: The EA feature, entailing the U-EGM amplitude, duration and overall morphology, is suitable in signal fingerprinting to characterize the arrhythmogenic substrate and contains additional information compared with amplitude alone. Further studies are required to fine-tune the EA and implement EA-based classification.