ArticleComputers in biology and medicine2021
Detection and removal of pacing artifacts prior to automated analysis of 12-lead ECG.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03210883 (Global Electrical Heterogeneity and Clinical Outcomes), which is not on this map. Cited by 5 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Global Electrical Heterogeneity and Clinical Outcomes
Who cites it
5 citing papers in PubMed.
- AI-Based QRS Onset Detection in the Early Ventricular Activation Site ECGs.IEEE journal of biomedical and health informatics · 2026Article
- Article
- ECG-based evaluation of ventricular synchrony in left bundle branch area pacing through characterization of the activation sequence.Scientific reports · 2025Article
- Automatic Identification of Involuntary Muscle Activity in Subacute Patients with Upper Motor Neuron Lesion at Rest-A Validation Study.Sensors (Basel, Switzerland) · 2023Article
- Demographic and Methodological Heterogeneity in Electrocardiogram Signals From Guinea Pigs.Frontiers in physiology · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundPacing artifacts must be excluded from the analysis of paced ECG waveform. This study aimed to develop and validate an algorithm to identify and remove the pacing artifacts on ECG and vectorcardiogram (VCG).
methodsWe developed a semi-automatic algorithm that identifies the onset and offset of a pacing artifact based on the VCG signal slope steepness and designed a graphical user interface that permits quality control and fine-tuning the constraining threshold values. We used 1054 ECGs from the retrospective, multicenter cohort study "Global Electrical Heterogeneity and Clinical Outcomes," including 3825 atrial and 10,031 ventricular pacing artifacts for the algorithm development and 22 ECGs including 108 atrial and 241 ventricular pacing artifacts for validation. Validation was performed per digital sample. We used the kappa-statistic of interrater agreement between manually labeled sample (ground-truth) and automated detection.
resultsThe constraining parameter values were for onset threshold 13.06 ± 6.21 μV/ms, offset threshold 34.77 ± 17.80 μV/ms, and maximum window size 27.23 ± 3.53 ms. The automated algorithm detected a digital sample belonging to pacing artifact with a sensitivity of 74.5% and specificity of 99.6% and classified correctly 98.8% of digital samples (ROC AUC 0.871; 95%CI 0.853-0.878). The kappa-statistic was 0.785, indicating substantial agreement. The agreement was on 98.81% digital samples, significantly (P < 0.00001) larger than the random agreement on 94.43% of digital samples.
conclusionsThe semi-automated algorithm can detect and remove ECG pacing artifacts with high accuracy and provide a user-friendly interface for quality control.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.