A Singular-Value-Based Map to Highlight Abnormal Regions Associated With Atrial Fibrillation Using High-Resolution Electrograms and Multi-Lead ECG

• Published in: IEEE transactions on biomedical engineering 2024-11, Vol.71 (11), p.3324-3336
• Main Authors: Moghaddasi, Hanie, Hendriks, Richard C., Hunyadi, Borbala, Knops, Paul, van Schie, Mathijs S, de Groot, Natasja M.S., van der Veen, Alle-Jan
• Format: Article
• Language: English
• Subjects: Action potentials; Algorithms; Atrial fibrillation; Atrial Fibrillation - diagnosis; Atrial Fibrillation - diagnostic imaging; Atrial Fibrillation - physiopathology; Cardiac arrhythmia; Computational modeling; Electric potential; Electrocardiography; Electrocardiography - methods; Electrodes; Fibrillation; Fractionation; High resolution; high-resolution electrograms; Humans; Mathematical models; Morphology; multi-lead body surface potentials; Propagation velocity; rank analysis; Signal Processing, Computer-Assisted; singular value decomposition; Spatial variations; Wave fronts; Waveforms
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2024.3420412

Objective: The severity of atrial fibrillation (AF) can be assessed from intra-operative epicardial measurements (high-resolution electrograms), using metrics such as conduction block (CB) and continuous conduction delay and block (cCDCB). These features capture differences in conduction velocity and wavefront propagation, but ignore complementary properties such as the morphology of the action potentials. In this work, we focus on such complementary properties, and derive features to detect variations in the atrial potential waveforms. Methods: We show that the spatial variation of atrial potential morphology during a single beat may be described by changes in the singular values of the epicardial measurement matrix. The method is non-parametric and requires little preprocessing. A corresponding singular value map points at areas subject to fractionation and block. Further, we developed an experiment where we simultaneously measure electrograms (EGMs) and a multi-lead ECG. Results: The captured data showed that the normalized singular values of the heartbeats during AF are higher than during SR, and that this difference is more pronounced for the (non-invasive) ECG data than for the EGM data, if the electrodes are positioned at favorable locations. Conclusion: Overall, the singular value-based features are a useful indicator to detect and evaluate AF. Significance: The proposed method might be beneficial for identifying electropathological regions in the tissue without estimating the local activation time.