Novel atrial pace-mapping technique based on dual-chamber electrograms to detect non-pulmonary vein foci.
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ABSTRACT:
SUBMITTER: Yamashita K
PROVIDER: S-EPMC10691955 | biostudies-literature | 2023 Oct
REPOSITORIES: biostudies-literature
Publications
Novel atrial pace-mapping technique based on dual-chamber electrograms to detect non-pulmonary vein foci.
Yamashita Kennosuke K Furuya Kenichi K Kumazawa Daiki D Mizuno Yosuke Y Onodera Kosuke K Nomura Takehiro T
HeartRhythm case reports 20230724 10
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Project description:BACKGROUND:Electrogram-guided ablation procedures have been proposed as an alternative strategy consisting of either mapping and ablating focal sources or targeting complex fractionated electrograms in atrial fibrillation (AF). However, the incomplete understanding of the mechanism of AF makes difficult the decision of detecting the target sites. To date, feature extraction from electrograms is carried out mostly based on the time-domain morphology analysis and non-linear features. However, their combination has been reported to achieve better performance. Besides, most of the inferring approaches applied for identifying the levels of fractionation are supervised, which lack of an objective description of fractionation. This aspect complicates their application on EGM-guided ablation procedures. METHODS:This work proposes a semi-supervised clustering method of four levels of fractionation. In particular, we make use of the spectral clustering that groups a set of widely used features extracted from atrial electrograms. We also introduce a new atrial-deflection-based feature to quantify the fractionated activity. Further, based on the sequential forward selection, we find the optimal subset that provides the highest performance in terms of the cluster validation. The method is tested on external validation of a labeled database. The generalization ability of the proposed training approach is tested to aid semi-supervised learning on unlabeled dataset associated with anatomical information recorded from three patients. RESULTS:A joint set of four extracted features, based on two time-domain morphology analysis and two non-linear dynamics, are selected. To discriminate between four considered levels of fractionation, validation on a labeled database performs a suitable accuracy (77.6 %). Results show a congruence value of internal validation index among tested patients that is enough to reconstruct the patterns over the atria to located critical sites with the benefit of avoiding previous manual classification of AF types. CONCLUSIONS:To the best knowledge of the authors, this is the first work reporting semi-supervised clustering for distinguishing patterns in fractionated electrograms. The proposed methodology provides high performance for the detection of unknown patterns associated with critical EGM morphologies. Particularly, obtained results of semi-supervised training show the advantage of demanding fewer labeled data and less training time without significantly compromising accuracy. This paper introduces a new method, providing an objective scheme that enables electro-physiologist to recognize the diverse EGM morphologies reliably.
| S-EPMC4845510 | biostudies-other
Project description:Atrial fibrillation (AF) is related to a very complex local electrical activity reflected in the rich morphology of intracardiac electrograms. The link between electrogram complexity and efficacy of the catheter ablation is unclear. We test the hypothesis that the Kolmogorov complexity of a single atrial bipolar electrogram recorded during AF within the coronary sinus (CS) at the beginning of the catheter ablation may predict AF termination directly after pulmonary vein isolation (PVI). The study population consisted of 26 patients for whom 30 s baseline electrograms were recorded. In all cases PVI was performed. If AF persisted after PVI, ablation was extended beyond PVs. Kolmogorov complexity estimated by Lempel–Ziv complexity and the block decomposition method was calculated and compared with other measures: Shannon entropy, AF cycle length, dominant frequency, regularity, organization index, electrogram fractionation, sample entropy and wave morphology similarity index. A 5 s window length was chosen as optimal in calculations. There was a significant difference in Kolmogorov complexity between patients with AF termination directly after PVI compared to patients undergoing additional ablation (p < 0.01). No such difference was seen for remaining complexity parameters. Kolmogorov complexity of CS electrograms measured at baseline before PVI can predict self-termination of AF directly after PVI.
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| S-EPMC8226311 | biostudies-literature
Project description:Focal sources are potential targets for atrial fibrillation (AF) catheter ablation, but they can be time-consuming and challenging to identify when unipolar electrograms (EGM) are numerous and complex. Our aim was to apply deep learning (DL) to raw unipolar EGMs in order to automate putative focal sources detection. We included 78 patients from the Focal Source and Trigger (FaST) randomized controlled trial that evaluated the efficacy of adjunctive FaST ablation compared to pulmonary vein isolation alone in reducing AF recurrence. FaST sites were identified based on manual classification of sustained periodic unipolar QS EGMs over 5-s. All periodic unipolar EGMs were divided into training (n = 10,004) and testing cohorts (n = 3,180). DL was developed using residual convolutional neural network to discriminate between FaST and non-FaST. A gradient-based method was applied to interpret the DL model. DL classified FaST with a receiver operator characteristic area under curve of 0.904 ± 0.010 (cross-validation) and 0.923 ± 0.003 (testing). At a prespecified sensitivity of 90%, the specificity and accuracy were 81.9 and 82.5%, respectively, in detecting FaST. DL had similar performance (sensitivity 78%, specificity 89%) to that of FaST re-classification by cardiologists (sensitivity 78%, specificity 79%). The gradient-based interpretation demonstrated accurate tracking of unipolar QS complexes by select DL convolutional layers. In conclusion, our novel DL model trained on raw unipolar EGMs allowed automated and accurate classification of FaST sites. Performance was similar to FaST re-classification by cardiologists. Future application of DL to classify FaST may improve the efficiency of real-time focal source detection for targeted AF ablation therapy.
| S-EPMC8360838 | biostudies-literature
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| S-EPMC11628854 | biostudies-literature
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| S-EPMC11781942 | biostudies-literature
Project description:Although a high-resolution three-dimensional mapping system has made it possible to treat complicated atrial tachyarrhythmia (AT), there remain cases that are difficult to diagnose and treat. However, when multiple different ATs alternately appear, mapping and diagnosis of those are more difficult. Parallel mapping module is well known as a good option to simultaneously map two or more different ATs when they alternately appear. When performing parallel mapping of two different ATs, one bipolar signal of the reference catheter is used as a timing reference and a cycle length filter is used for differentiating AT1, AT2, and others, including sinus rhythm, fusion beats, or catheter-induced premature atrial complex. Therefore, it has some limitations for differentiating multifocal ATs. We present a case wherein multifocal ATs were successfully eliminated by combining parallel mapping module and dual-chamber intra-cardiac pattern matching technique that we have previously reported.Learning objective▪Parallel mapping is a useful tool when two or more tachycardias alternately occur; however, it has some limitations.▪Dual-chamber intra-cardiac pattern matching technique, which combines right atrial and coronary sinus potentials, provides better discrimination than coronary sinus reference alone.▪By combining parallel mapping and dual-chamber intra-cardiac pattern matching, two or more atrial tachycardias could be automatically and simultaneously mapped.
| S-EPMC10865130 | biostudies-literature
Project description:Background and aimsAtrial extrasystoles (AES) provoke conduction disorders and may trigger episodes of atrial fibrillation (AF). However, the direction- and rate-dependency of electrophysiological tissue properties on epicardial unipolar electrogram (EGM) morphology is unknown. Therefore, this study examined the impact of spontaneous AES on potential amplitude, -fractionation, -duration, and low-voltage areas (LVAs), and correlated these differences with various degrees of prematurity and aberrancy.Methods and resultsIntra-operative high-resolution epicardial mapping of the right and left atrium, Bachmann's Bundle, and pulmonary vein area was performed during sinus rhythm (SR) in 287 patients (60 with AF). AES were categorized according to their prematurity index (>25% shortening) and degree of aberrancy (none, mild/opposite, moderate and severe). In total, 837 unique AES (457 premature; 58 mild/opposite, 355 moderate, and 154 severe aberrant) were included. The average prematurity index was 28% [12-45]. Comparing SR and AES, average voltage decreased (-1.1 [-1.2, -0.9] mV, P < 0.001) at all atrial regions, whereas the amount of LVAs and fractionation increased (respectively, +3.4 [2.7, 4.1] % and +3.2 [2.6, 3.7] %, P < 0.001). Only weak or moderate correlations were found between EGM morphology parameters and prematurity indices (R2 < 0.299, P < 0.001). All parameters were, however, most severely affected by either mild/opposite or severely aberrant AES, in which the effect was more pronounced in AF patients. Also, there were considerable regional differences in effects provoked by AES.ConclusionUnipolar EGM characteristics during spontaneous AES are mainly directional-dependent and not rate-dependent. AF patients have more direction-dependent conduction disorders, indicating enhanced non-uniform anisotropy that is uncovered by spontaneous AES.
| S-EPMC10401323 | biostudies-literature