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A Singular-Value-Based Map to Highlight Abnormal Regions Associated With Atrial Fibrillation Using High-Resolution
Insights
New singular value analysis of atrial fibrillation (AF) reveals distinct waveform variations. This method enhances detection and evaluation of AF, potentially identifying problematic tissue regions without complex calculations.
Area of Science:
- Cardiovascular Electrophysiology
- Biomedical Signal Processing
- Medical Diagnostics
Background:
- Atrial fibrillation (AF) severity is typically assessed using metrics like conduction block (CB) and continuous conduction delay and block (cCDCB) from epicardial electrograms.
- Existing methods focus on conduction velocity and wavefront propagation, overlooking crucial information from atrial action potential morphology.
- A need exists for novel analytical approaches to capture complementary electrophysiological properties for improved AF assessment.
Purpose of the Study:
- To derive and evaluate new features based on atrial potential waveform morphology for detecting variations associated with atrial fibrillation.
- To explore the utility of singular value decomposition of epicardial measurements for characterizing AF.
- To investigate the potential of this non-parametric method for identifying electropathological regions.
Main Methods:
- Utilized singular value decomposition (SVD) on epicardial measurement matrices to analyze spatial variations in atrial potential morphology during a single beat.
- Developed a non-parametric method requiring minimal preprocessing.
- Conducted simultaneous measurements of electrograms (EGMs) and multi-lead electrocardiograms (ECGs) to compare invasive and non-invasive data.
Main Results:
- Normalized singular values were significantly higher during AF compared to sinus rhythm (SR).
- The difference in normalized singular values between AF and SR was more pronounced in non-invasive ECG data than in EGM data under favorable electrode placement.
- Singular value maps effectively highlighted areas susceptible to conduction fractionation and block.
Conclusions:
- Singular value-based features derived from atrial potential morphology offer a valuable tool for detecting and evaluating atrial fibrillation.
- The proposed method provides a promising approach for identifying electropathological regions without the need for local activation time estimation.
- This technique enhances the understanding of AF pathophysiology by incorporating waveform morphology.
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.
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