Oregon Health & Science University, Knight Cardiovascular Institute, Portland, OR, USA.
Oregon Health & Science University, Knight Cardiovascular Institute, Portland, OR, USA.
Comput Biol Med. 2021 Jun;133:104396. doi: 10.1016/j.compbiomed.2021.104396. Epub 2021 Apr 19.
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).
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.
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.
The semi-automated algorithm can detect and remove ECG pacing artifacts with high accuracy and provide a user-friendly interface for quality control.
起搏伪迹必须从心电图（ECG）波形分析中排除。本研究旨在开发和验证一种算法，以识别和去除 ECG 和心向量图（VCG）上的起搏伪迹。
我们开发了一种半自动算法，该算法基于 VCG 信号斜率陡度来识别起搏伪迹的起始和结束，并设计了一个图形用户界面，允许进行质量控制和微调约束阈值。我们使用了回顾性多中心队列研究“全球电异质性和临床结局”中的 1054 份 ECG，包括 3825 份心房起搏伪迹和 10031 份心室起搏伪迹用于算法开发，以及 22 份 ECG，包括 108 份心房起搏伪迹和 241 份心室起搏伪迹用于验证。验证是按数字样本进行的。我们使用手动标记样本（金标准）和自动检测之间的kappa 一致性统计量。
约束参数值为起始阈值 13.06±6.21 μV/ms、结束阈值 34.77±17.80 μV/ms 和最大窗口大小 27.23±3.53 ms。自动算法检测到属于起搏伪迹的数字样本，其灵敏度为 74.5%，特异性为 99.6%，正确分类了 98.8%的数字样本（ROC AUC 0.871；95%CI 0.853-0.878）。kappa 统计量为 0.785，表明存在显著一致性。一致性为 98.81%的数字样本，显著（P<0.00001）大于 94.43%的数字样本的随机一致性。
半自动算法可以高精度地检测和去除 ECG 起搏伪迹，并提供用户友好的界面进行质量控制。
