Use of Correlation Waveform Analysis in Discrimination Between Anterograde and Retrograde Atrial Electrograms During Ventricular Tachycardia

Saba, Samir; Gorodeski, B S Revital; Yang, Bing; MacADAM, B S David; Link, Mark S.; Homoud, Munther K.; Estes, N.A. Mark; Wang, Paul J.

doi:10.1046/j.1540-8167.2001.00145.x

Cited by 9 publications

(5 citation statements)

References 6 publications

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“…The CWA and other template‐matching methods have been studied in patients who underwent EP studies to evaluate ventricular arrhythmias. The CWA approach was shown to be reliable in discriminating between anterograde and retrograde atrial activation, thus distinguishing sinus tachycardia from VT 9 …”

Section: Discussionmentioning

confidence: 99%

Rhythm Classification by Correlation‐Waveform Morphology Analysis of Atrial and Ventricular Electrogram Signals

Saba

Qin

Ganz

et al. 2005

Pacing Clinical Electrophis

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We tested the use of correlation-waveform analysis (CWA) of atrial and ventricular electrograms (EGMs) to distinguish ventricular tachycardia (VT) from supraventricular tachycardia (SVT). Patients undergoing electrophysiologic testing were enrolled. EGMs recorded during induced tachycardias were compared with EGMs recorded during sinus and paced rhythms, taken as templates, by assigning a CWA percent-match (CPM) score. Twenty-two patients were studied: 15 men and 7 women (mean age 48 years); 16 with SVT and 6 with VT. Using a sinus-rhythm template, the atrial CPM scores for SVT and VT were 66%+/- 20% and 93%+/- 5%, respectively (P = 0.0034). With a CPM-score cutoff of 85%, the sensitivity for correctly identifying VT was 100% and the specificity for rejection of SVT was 80%. The corresponding ventricular-CPM scores for SVT and VT were 81%+/- 12% and 72%+/- 24%, respectively (P = 0.13, cutoff = 65%, sensitivity = 50%, and specificity = 90%). Using a ventricular template with atrial pacing, the ventricular-CPM scores for SVT and VT were 87%+/- 9% and 76%+/- 14%, respectively (P = 0.028, cutoff = 70%, sensitivity = 50%, and specificity = 93%). Atrial CWA matching is superior to ventricular CWA matching in discriminating between SVT and VT. CWA matching in both chambers could potentially achieve better discrimination.

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Section: Discussionmentioning

confidence: 99%

Rhythm Classification by Correlation‐Waveform Morphology Analysis of Atrial and Ventricular Electrogram Signals

Saba

Qin

Ganz

et al. 2005

Pacing Clinical Electrophis

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“…30 Using intracardiac atrial electrograms, Saba et al demonstrated that CWA could distinguish retrograde atrial activity from normal sinus rhythm. 19 SippensGroenewegen et al used an atlas of paced P-wave integral maps to localize the site of origin of right atrial arrhythmias. 31 It is quite possible that a more extensive database of surface P-wave morphologies may allow more accurate localization of ectopic sites including the pulmonary veins and superior vena cava.…”

Section: Discussionmentioning

confidence: 99%

Testing of a New T‐Wave Subtraction Algorithm as an Aid to Localizing Ectopic Atrial Beats

Marenco

Nakagawa

Yang³

et al. 2003

Noninvasive Electrocardiol

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“…Hence, the first eight cardiac cycles are for initialization. C ecg : Waveform correlation [20] , [37] . An ensemble averaged template waveform is generated by averaging the re-sampled time-series beat cycles.…”

Section: Methods and Proceduresmentioning

confidence: 99%

“…In particular to our study, we use personalized features to construct input to an ANN. More specifically, the selected features include heart rate, heart rate variability [19] , waveform correlation [20] , HS associated waveform power intensity [21] and wavelet-based time-frequency coefficients [22] , [23] . To obtain the corrected quiescence, we employ a linear combination of predicted quiescence from ECG and SCG whereby weights are outputs from the ANN.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Seismocardiography (SCG)-ECG Multimodal Framework for Cardiac Gating Using Artificial Neural Networks

Yao

Tridandapani

Auffermann

et al. 2018

IEEE J. Transl. Eng. Health Med.

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To more accurately trigger data acquisition and reduce radiation exposure of coronary computed tomography angiography (CCTA), a multimodal framework utilizing both electrocardiography (ECG) and seismocardiography (SCG) for CCTA prospective gating is presented. Relying upon a three-layer artificial neural network that adaptively fuses individual ECG- and SCG-based quiescence predictions on a beat-by-beat basis, this framework yields a personalized quiescence prediction for each cardiac cycle. This framework was tested on seven healthy subjects (age: 22-48; m/f: 4/3) and eleven cardiac patients (age: 31-78; m/f: 6/5). Seventeen out of 18 benefited from the fusion-based prediction as compared to the ECG-only-based prediction, the traditional prospective gating method. Only one patient whose SCG was compromised by noise was more suitable for ECG-only-based prediction. On average, our fused ECG-SCG-based method improves cardiac quiescence prediction by 47% over ECG-only-based method; with both compared against the gold standard, B-mode echocardiography. Fusion-based prediction is also more resistant to heart rate variability than ECG-only- or SCG-only-based prediction. To assess the clinical value, the diagnostic quality of the CCTA reconstructed volumes from the quiescence derived from ECG-, SCG- and fusion-based predictions were graded by a board-certified radiologist using a Likert response format. Grading results indicated the fusion-based prediction improved diagnostic quality. ECG may be a sub-optimal modality for quiescence prediction and can be enhanced by the multimodal framework. The combination of ECG and SCG signals for quiescence prediction bears promise for a more personalized and reliable approach than ECG-only-based method to predict cardiac quiescence for prospective CCTA gating.

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Use of Correlation Waveform Analysis in Discrimination Between Anterograde and Retrograde Atrial Electrograms During Ventricular Tachycardia

Cited by 9 publications

References 6 publications

Rhythm Classification by Correlation‐Waveform Morphology Analysis of Atrial and Ventricular Electrogram Signals

Rhythm Classification by Correlation‐Waveform Morphology Analysis of Atrial and Ventricular Electrogram Signals

Testing of a New T‐Wave Subtraction Algorithm as an Aid to Localizing Ectopic Atrial Beats

An Adaptive Seismocardiography (SCG)-ECG Multimodal Framework for Cardiac Gating Using Artificial Neural Networks

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