ECGVEDNET: A Variational Encoder-Decoder Network for ECG Delineation in Morphology Variant ECGs
ECGVEDNET: A Variational Encoder-Decoder Network for ECG Delineation in Morphology Variant ECGs
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DOI：
10.1109/tbme.2024.3363077
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发表时间：
2024-07-01
影响因子：
4.6
通讯作者：
Li，Xin
中科院分区：
文献类型：
--
作者：
Chen，Long;Jiang，Zheheng;Li，Xin
心电图描记是确定心电图段的基准点，在心血管疾病的诊断和护理中起着重要的作用。虽然在文献中已经部署了深度描绘框架，但仍有几个因素阻碍了它们的发展：（a）数据可用性：深度学习模型的泛化能力受到可用数据量的限制;（B）形态变化：即使在同一个人体内，ECG复合波也会发生变化，这会降低传统深度学习模型的性能。为了解决这些问题，我们提出了一个大规模的12导联心电图数据集，ICDIRS，训练和评估一种新的深度描绘模型-ECGVEDNET。ICDIRS是一个大型ECG数据集，包含156，145个QRS起始注释和156，145个T峰注释。ECGVEDNET是一种新颖的变分编码器-解码器网络，旨在解决形态变化。在ECGVEDNET中，我们构建了一个规则化的潜在空间，其中ECG的潜在特征遵循规则分布，并且呈现比原始数据空间更小的形态变化。最后，提出了一个迁移学习框架，将ICDIRS上学到的知识迁移到较小的数据集。在ICDIRS上，ECGVEDNET在5/10 ms容差内实现了QRS起点的准确度为86.28%/88.31%，在5/10 ms容差内实现了T峰的准确度为89.94%/91.16%。在QTDB上，计算的QRS起点和T峰值的平均时间误差分别为-1.868.02 ms和-0.5012.96 ms，在大型和小型数据集上都达到了最先进的性能。一旦被接受，我们将在ICDIRS上发布源代码和预训练模型。
Electrocardiogram (ECG) delineation to identify the fiducial points of ECG segments, plays an important role in cardiovascular diagnosis and care. Whilst deep delineation frameworks have been deployed within the literature, several factors still hinder their development: (a) data availability: the capacity of deep learning models to generalise is limited by the amount of available data; (b) morphology variations: ECG complexes vary, even within the same person, which degrades the performance of conventional deep learning models. To address these concerns, we present a large-scale 12-leads ECG dataset, ICDIRS, to train and evaluate a novel deep delineation model-ECGVEDNET. ICDIRS is a large-scale ECG dataset with 156,145 QRS onset annotations and 156,145 T peak annotations. ECGVEDNET is a novel variational encoder-decoder network designed to address morphology variations. In ECGVEDNET, we construct a well-regularized latent space, in which the latent features of ECG follow a regular distribution and present smaller morphology variations than in the raw data space. Finally, a transfer learning framework is proposed to transfer the knowledge learned on ICDIRS to smaller datasets. On ICDIRS, ECGVEDNET achieves accuracy of 86.28%/88.31% within 5/10 ms tolerance for QRS onset and accuracy of 89.94%/91.16% within 5/10 ms tolerance for T peak. On QTDB, the average time errors computed for QRS onset and T peak are −1.868.02 ms and −0.5012.96 ms, respectively, achieving state-of-the-art performances on both large and small-scale datasets. We will release the source code and the pre-trained model on ICDIRS once accepted.