Computational Biomedicine Lab, Computer and Information Sciences, University of Delaware, Newark, Delaware, USA.
Division of Cardiology, Hypertrophic Cardiomyopathy Center of Excellence, University of California San Francisco, San Francisco, California, USA.
J Am Med Inform Assoc. 2022 Oct 7;29(11):1879-1889. doi: 10.1093/jamia/ocac122.
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.
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.
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.
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.
在肥厚型心肌病（HCM）中，冲动传播和心脏复极异常很常见，导致 12 导联心电图（ECG）异常。计算 ECG 分析可以识别电生理和结构重构，并预测心律失常。这需要准确的 ECG 分段。目前尚不清楚使用主要包含正常心跳注释的数据集开发的当前分段方法在 HCM 中表现如何。在这里，我们提出了一种有效的分段方法，可用于有效识别 12 导联 HCM ECG 中的 ECG 波。
我们开发了（1）一种基于网络的工具，该工具允许 3 位专家手动注释 P、P'、QRS、R'、S'、T、T'、U、J、epsilon 波、QRS 复合模糊和心房颤动，以及（2）一种易于实施的分段方法，可有效识别正常和异常心跳中的 ECG 波。我们的方法在 131 个 12 导联 HCM ECG 和 2 个公共 ECG 集上进行了测试，以评估其在非 HCM ECG 中的性能。
在 HCM 数据集上，当检测 QRS 复合波和 T 偏移时，我们的方法分别获得了 99.2%和 98.1%的灵敏度和 92%和 95.3%的阳性预测值，明显优于以前用于 HCM 分析的最新分段方法。在公共 ECG 集上，当检测 P 波起始和峰值、T 偏移和 QRS 波起始和峰值时，它在阳性预测值和分段误差方面明显优于 3 种最先进的方法。在其他任务中，它的表现与其他方法相似。
我们的方法在 HCM 数据集上准确地识别了 ECG 波，优于一种最先进的方法，并在正常/非 HCM ECG 集上表现出与其他方法相似的良好性能。
