Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433, People's Republic of China.
Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai, 200093, People's Republic of China.
J Interv Card Electrophysiol. 2024 Apr;67(3):457-470. doi: 10.1007/s10840-023-01551-7. Epub 2023 Apr 25.
Premature ventricular contraction (PVC) is a type of cardiac arrhythmia that originates from ectopic pacemaker in the ventricles. The localization of the origin of PVC is essential for successful catheter ablation. However, most studies on non-invasive PVC localization focus on elaborate localization in specific regions of the ventricle. This study aims to propose a machine learning algorithm based on 12-lead electrocardiogram (ECG) data that can improve the accuracy of PVC localization in the whole ventricle.
We collected 12-lead ECG data from 249 patients with spontaneous or pacing-induced PVCs. The ventricle was divided into 11 segments. In this paper, we propose a machine learning method consisting of two consecutive classification steps. In the first classification step, each PVC beat was labeled to one of the 11 ventricular segments using six features, including a newly proposed morphological feature called "Peak_index." Four machine learning methods were tested for comparative multi-classification performance and the best classifier result was kept to the next step. In the second classification step, a binary classifier was trained using a smaller combination of features to further differentiate segments that are easily confused.
The Peak_index as a proposed new classification feature combined with other features is suitable for whole ventricle classification by machine learning methods. The test accuracy of the first classification reached 75.87%. It is shown that a second classification for confusable categories can improve the classification results. After the second classification, the test accuracy reached 76.84%, and when a sample classified into adjacent segments was considered correct, the test "rank accuracy" was improved to 93.49%. The binary classification corrected 10% of the confused samples.
This paper proposes a "two-step classification" method to localize the origin of PVC beats into the 11 regions of the ventricle using non-invasive 12-lead ECG. It is expected to be a promising technique to be used in clinical settings to help guide ablation procedures.
室性期前收缩（PVC）是一种起源于心室异位起搏点的心律失常。PVC 起源的定位对于成功的导管消融至关重要。然而，大多数关于非侵入性 PVC 定位的研究都集中在心室特定区域的精细定位上。本研究旨在提出一种基于 12 导联心电图（ECG）数据的机器学习算法，以提高整个心室 PVC 定位的准确性。
我们从 249 例自发性或起搏诱导 PVC 患者中收集了 12 导联 ECG 数据。心室被分为 11 个节段。在本文中，我们提出了一种由两个连续分类步骤组成的机器学习方法。在第一步分类中，使用包括新提出的形态特征“峰值指数”在内的六个特征，将每个 PVC 搏动标记为 11 个心室节段之一。测试了四种机器学习方法的多类分类性能，并保留了最佳分类器结果进入下一步。在第二步分类中，使用较小的特征组合训练一个二分类器，以进一步区分易混淆的节段。
峰值指数作为一种新的分类特征与其他特征相结合，适合使用机器学习方法进行整个心室分类。第一步分类的测试准确率达到 75.87%。结果表明，对易混淆类别进行二次分类可以提高分类结果。经过二次分类，测试准确率达到 76.84%，当将一个被分类到相邻节段的样本视为正确时，测试“等级准确率”提高到 93.49%。二分类纠正了 10%的混淆样本。
本文提出了一种“两步分类”方法，使用非侵入性 12 导联 ECG 将 PVC 搏动起源定位到心室的 11 个区域。有望成为一种有前途的技术，用于临床指导消融过程。
