Detection and removal of pacing artifacts prior to automated analysis of 12-lead ECG

Kazi T. Haq, Neeraj Javadekar, Larisa G. Tereshchenko

    Research output: Contribution to journalArticlepeer-review

    3 Scopus citations


    Background: Pacing 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). Methods: We 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. Results: The 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. Conclusions: The semi-automated algorithm can detect and remove ECG pacing artifacts with high accuracy and provide a user-friendly interface for quality control.

    Original languageEnglish (US)
    Article number104396
    JournalComputers in Biology and Medicine
    StatePublished - Jun 2021


    • ECG
    • Pacemaker
    • Pacing artifact
    • Signal processing

    ASJC Scopus subject areas

    • Computer Science Applications
    • Health Informatics


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