Diagnostic accuracy of pace spikes in the electrocardiogram to diagnose paced rhythm

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

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.

A low-pass filter of 300 Hz improved the detection of pacemaker spike on remote and bedside electrocardiogram

Background:

The current upper-frequency cutoff of 150 Hz sometimes causes loss of pacemaker spike and misdiagnosis. We hypothesized that low-pass filter (LPF) other than 150 Hz could improve the detection of pacemaker spike. This study aimed to examine the effect of different LPF on pacemaker spike detection in remote and bedside electrocardiogram (ECG).

Methods:

Patients with permanent pacemaker implantation were included during routine follow-up. Standard 12-lead ECGs at 6 different upper-frequency cutoff (40, 100, 150, 200, 300, and 400 Hz) were collected. All ECGs were then transmitted to the remote clinic center. Ventricular and atrial pacing were analyzed by 2 independent medical practitioners.

Results:

A total of 88 patients’ ECGs were analyzed (mean age 73.8 ± 10.2 years and 85 with dual-chamber pacemakers). About 75.3% (64/85) of patients were diagnosed as atrial pacing by pacemaker programming. Among 6 different upper-frequency cutoff, the 300 Hz turned out to perform best in detecting atrial-paced spike (area under the curve [AUC] = 0.73, 95% confidence interval [CI]: 0.61–0.84 vs. 0.56, 95% CI: 0.61–0.84 at 150 Hz; P = 0.002) on bedside ECGs. Using programming as the golden standard, the 300 Hz LPF has a sensitivity of 59.4%, specificity of 85.7%, positive predictive value of 92.7% and negative predictive value of 40.9% on bedside ECGs. As for the ventricular pacing, the 300 Hz LPF also had a higher accuracy (AUC = 0.93; 95% CI = 0.84–1.00) than that at 150 Hz (AUC = 0.86; 95% CI: 0.77–0.94; P < 0.001) in detecting ventricular-paced spike on bedside ECGs. The results of remote ECGs were similar with bedside ECGs.

Conclusions:

A filter of 300 Hz cutoff may be recommended for ECG spike detection. With the recommended parameter, remote ECG can perform as well as bedside ECG.