Computerized interpretation of the paced ECG

Greenhut, Saul E.; Jenkins, Janice M.; DiCarlo, Lorenzo A.

doi:10.1016/s0022-0736(10)80037-8

Cited by 4 publications

(2 citation statements)

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“…Insufficient quality of computer reading on ECGs with an electronic pacemaker was analyzed before, and new algorithms were suggested [18]. In the dataset of Endou et al [5] electronic pacemakers were the weakest area of interpretation, with the sensitivity of only 3% (computer did not recognize 32 electronic pacemakers out of 33).…”

Section: Discussionmentioning

confidence: 99%

Common errors in computer electrocardiogram interpretation

Guglin

Thatai

2006

International Journal of Cardiology

108

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Section: Discussionmentioning

confidence: 99%

Common errors in computer electrocardiogram interpretation

Guglin

Thatai

2006

International Journal of Cardiology

108

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“…An electrocardiogram (ECG) is widely used to determine the heart rhythm and to evaluate the performance of pacemaker functioning, especially in emergency settings. [3] Atrial-paced and ventricular-paced rhythm and atrioventricular (AV) dual-paced rhythm are included in the list of core primary ECG diagnostic statements, endorsed by the American Heart Association (AHA), the American College of Cardiology (ACC), the Heart Rhythm Society (HRS), and the International Society for Computerized Electrocardiography (ISCE). [4] Notably, the ECG diagnostic standards enforced the rule that no secondary statements can accompany the primary diagnostic statement of paced rhythm or paced complexes.…”

Section: Introductionmentioning

confidence: 99%

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

Haq

Javadekar

Tereshchenko

2021

Preprint

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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.MethodsWe developed a semi-automatic algorithm that identifies the onset and offset of a pacing artifact based on the ECG signal’ slope steepness and designed a graphical user interface that permits quality control and fine-tuning the constraining threshold values. We used 1,054 ECGs from the retrospective, multicenter cohort study “Global Electrical Heterogeneity and Clinical Outcomes,” including 3,825 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.HighlightsWe developed and validated a semi-automated algorithm to detect and remove pacing spike artifacts from a digital ECG signal.The semi-automated algorithm can detect and remove pacing spike artifacts with high accuracy and provide a user-friendly interface for quality control.

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