Key result
Novel ECG segmentation method outperforms existing tools with ~99% QRS detection sensitivity in HCM.
Why the study?
It was unknown whether existing ECG segmentation methods, developed mostly on normal heartbeats, perform well on abnormal 12-lead ECGs in hypertrophic cardiomyopathy.
A novel ECG segmentation method accurately identifies ECG waves in hypertrophic cardiomyopathy, outperforming existing state-of-the-art methods.
OBJECTIVE: Abnormalities in impulse propagation and cardiac repolarization are frequent in hypertrophic cardiomyopathy (HCM), leading to abnormalities in 12-lead electrocardiograms (ECGs). Computational ECG analysis can identify electrophysiological and structural remodeling and predict arrhythmias. This requires accurate ECG segmentation. It is unknown whether current segmentation methods developed using datasets containing annotations for mostly normal heartbeats perform well in HCM. Here, we present a segmentation method to effectively identify ECG waves across 12-lead HCM ECGs. METHODS: We develop (1) a web-based tool that permits manual annotations of P, P', QRS, R', S', T, T', U, J, epsilon waves, QRS complex slurring, and atrial fibrillation by 3 experts and (2) an easy-to-implement segmentation method that effectively identifies ECG waves in normal and abnormal heartbeats. Our method was tested on 131 12-lead HCM ECGs and 2 public ECG sets to evaluate its performance in non-HCM ECGs. RESULTS: Over the HCM dataset, our method obtained a sensitivity of 99.2% and 98.1% and a positive predictive value of 92% and 95.3% when detecting QRS complex and T-offset, respectively, significantly outperforming a state-of-the-art segmentation method previously employed for HCM analysis. Over public ECG sets, it significantly outperformed 3 state-of-the-art methods when detecting P-onset and peak, T-offset, and QRS-onset and peak regarding the positive predictive value and segmentation error. It performed at a level similar to other methods in other tasks. CONCLUSION: Our method accurately identified ECG waves in the HCM dataset, outperforming a state-of-the-art method, and demonstrated similar good performance as other methods in normal/non-HCM ECG sets.
No takes yet. Share an insight, caveat, or question.
Nezamabadi et al. (2022) studied Hypertrophic cardiomyopathy (n=131). Novel ECG segmentation method vs. State-of-the-art segmentation methods was evaluated on Sensitivity and positive predictive value for detecting QRS complex and T-offset. A novel ECG segmentation method accurately identified ECG waves in 131 hypertrophic cardiomyopathy ECGs, achieving 99.2% sensitivity for QRS complex detection and outperforming existing methods.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: