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

Marenco, John P.; Nakagawa, Hiroshi; Yang, Shawn; MacAdam, David L.; Xu, Lucien; He, Ding Sheng; Link, Mark S.; Homoud, Munther K.; Estes, N.A. Mark; Wang, Paul J.

doi:10.1046/j.1542-474x.2003.08109.x

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…Ventricular activity suppression methods aim to isolate the P-wave by removing ventricular activity from the ECG signal. A straightforward approach involves directly subtracting a template QRS-T complex from the original signal (Stridh and Sommo, 2001; Sippensgroe-newegen et al, 2001; Marenco et al, 2003). This method requires R-wave positions for accurate template localization and often utilizes dynamic time warping to adapt the template shape to account for respiration and heart rate variations.…”

Section: Related Workmentioning

confidence: 99%

Learning Discriminative Representations of Superimposed P waves With Weakly-Supervised Temporal Contrastive Learning

Hejc,

Redina,

Pospisil

et al. 2024

Preprint

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Electrocardiography (ECG) wave morphology and timing provide critical diagnostic information for diagnosing arrhythmias and conduction abnormalities, allowing risk stratification for various cardiac diseases. However, accurate extraction of these features becomes challenging in the presence of superimposed waves from distinct cardiac chambers, a common occurrence during pathological rhythms. This work proposes a novel Surrogate-boosted Temporal Contrastive Representation Learning (S-TCRL) framework to address this challenge. S-TCRL leverages weak labels, readily obtainable from invasive catheter examinations, to extract latent representations of superimposed P waves. We reformulate the problem from object-wise to sample-wise incomplete information by employing surrogate labels. A 1D fully-convolutional feature pyramid network (FPN) extracts multi-scale features from ECG signals. These features are segmented into equal-sized temporal regions, whose labels are inferred from individual samples using a multiple-instance learning (MIL) paradigm. Non-sequential embeddings are generated to facilitate alignment-free cosine similarity estimation. A temperature-scaled cross-entropy loss function minimizes the distance between embeddings of similar regions (likely containing P waves) while maximizing the distance between dissimilar ones. The framework's efficacy is evaluated on a custom ECG dataset comprising 3265 short-term recordings from 708 individuals undergoing catheter ablation. S-TCRL achieves significant improvement in the downstream P wave segmentation task compared to two baseline MIL methods. The average recall and precision for both P wave boundaries reach 70.0% and 80.0%, respectively, exceeding the baselines' 63.5% and 67.5%. These results demonstrate the potential of S-TCRL for embedding representation of superimposed P waves and its potential generalizability to other tasks like arrhythmia classification.

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Section: Related Workmentioning

confidence: 99%

Learning Discriminative Representations of Superimposed P waves With Weakly-Supervised Temporal Contrastive Learning

Hejc,

Redina,

Pospisil

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Catheter Ablation of Atrial Fibrillation Initiated by Non–Pulmonary Vein Triggers

Lo,

Chen

2025

Huang's Catheter Ablation of Cardiac Arrhythmias

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

Abstract: An ectopic atrial P wave obscured by a coincident QRS-T wave complex can be accurately uncovered using this new algorithm. Addition of this technique to existing methods may improve the diagnosis of atrial arrhythmias and aid in the localization and ablation of ectopic atrial foci.

Cited by 2 publications

References 33 publications

Learning Discriminative Representations of Superimposed P waves With Weakly-Supervised Temporal Contrastive Learning

Learning Discriminative Representations of Superimposed P waves With Weakly-Supervised Temporal Contrastive Learning

Catheter Ablation of Atrial Fibrillation Initiated by Non–Pulmonary Vein Triggers

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