Zhou Xue, Zhu Xin, Nakamura Keijiro, Noro Mahito
Biomedical Information Engineering Lab, The University of Aizu, Aizu-Wakamatsu, Fukushima 965-8580, Japan.
Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo 153-8515, Japan.
Life (Basel). 2021 Sep 26;11(10):1013. doi: 10.3390/life11101013.
The electrocardiogram (ECG) is widely used for cardiovascular disease diagnosis and daily health monitoring. Before ECG analysis, ECG quality screening is an essential but time-consuming and experience-dependent work for technicians. An automatic ECG quality assessment method can reduce unnecessary time loss to help cardiologists perform diagnosis. This study aims to develop an automatic quality assessment system to search qualified ECGs for interpretation. The proposed system consists of data augmentation and quality assessment parts. For data augmentation, we train a conditional generative adversarial networks model to get an ECG segment generator, and thus to increase the number of training data. Then, we pre-train a deep quality assessment model based on a training dataset composed of real and generated ECG. Finally, we fine-tune the proposed model using real ECG and validate it on two different datasets composed of real ECG. The proposed system has a generalized performance on the two validation datasets. The model's accuracy is 97.1% and 96.4%, respectively for the two datasets. The proposed method outperforms a shallow neural network model, and also a deep neural network models without being pre-trained by generated ECG. The proposed system demonstrates improved performance in the ECG quality assessment, and it has the potential to be an initial ECG quality screening tool in clinical practice.
心电图（ECG）广泛应用于心血管疾病诊断和日常健康监测。在进行心电图分析之前，对心电图质量进行筛查对于技术人员来说是一项必不可少但耗时且依赖经验的工作。一种自动心电图质量评估方法可以减少不必要的时间损耗，以帮助心脏病专家进行诊断。本研究旨在开发一种自动质量评估系统，以筛选出合格的心电图进行解读。所提出的系统由数据增强和质量评估两部分组成。对于数据增强，我们训练一个条件生成对抗网络模型来获得一个心电图片段生成器，从而增加训练数据的数量。然后，我们基于由真实和生成的心电图组成的训练数据集预训练一个深度质量评估模型。最后，我们使用真实心电图对所提出的模型进行微调，并在由真实心电图组成的两个不同数据集上对其进行验证。所提出的系统在两个验证数据集上具有泛化性能。该模型在两个数据集上的准确率分别为97.1%和96.4%。所提出的方法优于浅层神经网络模型，也优于未经过生成心电图预训练的深度神经网络模型。所提出的系统在心电图质量评估中表现出了改进的性能，并且有潜力成为临床实践中的一种初始心电图质量筛查工具。
