UNASSIGNED: The deep learning-based semantic segmentation model was trained using 265 12-lead ECG recordings from 84 patients with frequent PVCs. The model classified each ECG sampling time into four categories: background (BG), sinus rhythm (SR), PVC originating from the left ventricular outflow tract (PVC-L), and PVC originating from the right ventricular outflow tract (PVC-R). Based on the ECG segmentation results, a rule-based algorithm classified ECG recordings into three categories: PVC-L, PVC-R, as well as Neutral, which is a group for the recordings requiring the physician\'s careful assessment before separating them into PVC-L and PVC-R. The proposed method was evaluated with a public dataset which was used in previous research.
UNASSIGNED: The evaluation of the proposed method achieved neutral rate, accuracy, sensitivity, specificity, F1-score, and area under the curve of 0.098, 0.932, 0.963, 0.882, 0.945, and 0.852 on a private dataset, and 0.284, 0.916, 0.912, 0.930, 0.943, and 0.848 on a public dataset, respectively. These quantitative results indicated that the proposed method outperformed almost all previous studies, although a significant number of recordings resulted in requiring the physician\'s assessment.
UNASSIGNED: The feasibility of explainable localization of premature ventricular contraction was demonstrated using deep learning-based semantic segmentation of 12-lead ECG.Clinical trial registration: M26-148-8.
基于深度学习的语义分割模型是使用来自84例频繁PVC患者的265个12导联ECG记录进行训练的。该模型将每个ECG采样时间分为四类:背景(BG),窦性心律(SR),源自左心室流出道的PVC(PVC-L),和源自右心室流出道的PVC(PVC-R)。根据心电分割结果,基于规则的算法将ECG记录分为三类:PVC-L,PVC-R,以及中立,这是一组录音,需要医生仔细评估,然后再将它们分成PVC-L和PVC-R。所提出的方法是用以前研究中使用的公共数据集进行评估的。
■对所提出的方法的评估实现了中性率,准确度,灵敏度,特异性,F1分数,以及私有数据集上0.098、0.932、0.963、0.882、0.945和0.852的曲线下面积,公共数据集上的0.284、0.916、0.912、0.930、0.943和0.848,分别。这些定量结果表明,所提出的方法优于几乎所有以前的研究,尽管大量的记录导致需要医生的评估。
■使用基于深度学习的12导联ECG语义分割证明了可解释定位室性早搏的可行性。临床试验注册:M26-148-8。