Automated detection and mapping of electrical activation when electrogram morphology is complex

Treo, Ernesto Federico; Cervantes, Daniel Sandoval; Ciaccio, Edward J.

doi:10.1016/j.bspc.2012.04.006

Cited by 9 publications

(7 citation statements)

References 25 publications

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“…Template matching is an automated process of comparing segments of an electrogram signal, or specific electrogram complexes, to a library of deflection morphologies. The library of reference complexes may be generated mathematically [91,92] , or directly from actual electrogram recordings [93] . The approach is primarily targeted at identifying activation times during fibrillatory activity where multi-deflection complexes, whose morphologies vary over time, are present.…”

Section: Local Activation Timementioning

confidence: 99%

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Techniques for automated local activation time annotation and conduction velocity estimation in cardiac mapping

Cantwell

Roney

et al. 2015

Computers in Biology and Medicine

167

137

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Measurements of cardiac conduction velocity provide valuable functional and structural insight into the initiation and perpetuation of cardiac arrhythmias, in both a clinical and laboratory context. The interpretation of activation wavefronts and their propagation can identify mechanistic properties of a broad range of electrophysiological pathologies. However, the sparsity, distribution and uncertainty of recorded data make accurate conduction velocity calculation difficult. A wide range of mathematical approaches have been proposed for addressing this challenge, often targeted towards specific data modalities, species or recording environments. Many of these algorithms require identification of activation times from electrogram recordings which themselves may have complex morphology or low signal-to-noise ratio. This paper surveys algorithms designed for identifying local activation times and computing conduction direction and speed. Their suitability for use in different recording contexts and applications is assessed.

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Section: Local Activation Timementioning

confidence: 99%

“…These approaches have been applied with some success for signals recorded during atrial fibrillation, but can struggle to correctly annotate multi-component signals. In addition to the correlation function, the use of an error estimator may improve the robustness of the activation detection [93] .…”

Section: Local Activation Timementioning

confidence: 99%

Techniques for automated local activation time annotation and conduction velocity estimation in cardiac mapping

Cantwell

Roney

et al. 2015

Computers in Biology and Medicine

167

137

View full text Add to dashboard Cite

show abstract

“…The vast majority of automation studies focus on activation and substrate mapping [22] [23]. But, as we previously discussed, recent studies showing efficacy of PEFA leads us to believe this might be a superior method for exposing and isolating fractionation a signature of slow conduction.…”

Section: Discussionmentioning

confidence: 99%

“…proached by several other investigators[20]-[23]. However, our method offers severalkey improvements.…”

mentioning

confidence: 93%

Novel Automated Paced Fractionation Detection Algorithm for Ablating Ventricular Tachycardia

Gupta¹,

Shariat²,

Baetz-Dougan³

et al. 2016

JBiSE

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Catheter ablation therapy has become a key intervention in treatment of ventriculartachycardia (VT). However, current fractionation mapping methods used to isolate the ablation targets in VT patients are done manually, and are therefore time consuming. They also have limited success rates (50% recurrence rate within 2 years). We present a fully automated fractionation detection algorithm for patients with VT which expands on previously defined fractionation features and which substantially decreases associated study times. Paced electrogram signals were collected from six patients during electrophysiologic study according to a modified paced electrogram fractionation analysis protocol. Data were exported and analyzed offline using custom written software. Electrograms from right ventricular pacing catheter were used as reference. Surface electrograms, along with ventricular geometry and relative catheter locations, were used to identify physiological interference and physiologically irrelevant features. A total of 264 electrograms, collected from a roving catheter, were manually and automatically annotated for fractionation as defined by three features: conduction time (CT), electrogram duration (ED), and number of deflections (ND). Of these, 60 were selected manually to have no discernable features and were successfully discarded by our algorithm; yielding a specificity of 100%. Of the remaining 204, 16 were erroneously discarded by our algorithm; yielding a sensitivity of 92.16%. A comparison between annotations showed correlations of 0.98, 0.97, and 0.94 for AL, ED, and ND respectively.

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“…have already reported the unsuitability of Botteron's approach for analyzing CFAEs . Indeed, these authors are currently engaged in characterizing CFAE signals , and developing optimal techniques for CFAEs analysis …”

Section: Discussionmentioning

confidence: 99%

On the Preprocessing of Atrial Electrograms in Atrial Fibrillation: Understanding Botteron's Approach

Castells

Cervigón

Millet

2013

Pacing Clinical Electrophis

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Automated detection and mapping of electrical activation when electrogram morphology is complex

Cited by 9 publications

References 25 publications

Techniques for automated local activation time annotation and conduction velocity estimation in cardiac mapping

Techniques for automated local activation time annotation and conduction velocity estimation in cardiac mapping

Novel Automated Paced Fractionation Detection Algorithm for Ablating Ventricular Tachycardia

On the Preprocessing of Atrial Electrograms in Atrial Fibrillation: Understanding Botteron's Approach

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