Liu Han, Zhao Zhengbo, Chen Xiao, Yu Rong, She Qiang
Department of Neurology, Jiulongpo District Peoples Hospital, Chongqing, 400050, China.
Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, 74 Linjiang Road, Chongqing, 400010, China.
Comput Methods Programs Biomed. 2020 Nov;196:105639. doi: 10.1016/j.cmpb.2020.105639. Epub 2020 Jul 4.
Morphological diagnosis is a basic clinical task of the short-duration 12-lead electrocardiogram (ECG). Due to the scarcity of positive samples and other factors, there is currently no algorithm that is comparable to human experts in ECG morphological recognition. Our objective is to develop an ECG specialist-level deep learning method that can accurately identify ten ECG morphological abnormalities in real scene data.
We established a short-duration 12-lead ECG image dataset that consists of approximately 200,000 samples. To address the problems with small positive samples, a data augmentation method was proposed. We solved it by interpolating in the latent space of the vector quantized variational autoencoder (VQ-VAE) and generating new samples via sampling. The trained final classifier, general doctors, and ECG specialists evaluated the diagnostic performance on a test set that consisted of 1000 samples.
Relative to that of unaugmented data, the F1 score was improved by 0-6%. Compared with ECG specialists, the deep neural network achieved higher F1 scores and sensitivity in most categories.
Our method can improve the classification performance of ECG data with insufficient positive samples and reach the level of ECG specialists. This approach can provide specialized reference opinions for ordinary clinicians and reduce the errors of ECG specialists.
形态学诊断是短程12导联心电图（ECG）的一项基本临床任务。由于阳性样本稀缺等因素，目前尚无在ECG形态学识别方面可与人类专家相媲美的算法。我们的目标是开发一种能在真实场景数据中准确识别十种ECG形态异常的深度学习方法，达到ECG专家水平。
我们建立了一个由约200,000个样本组成的短程12导联ECG图像数据集。为解决阳性样本少的问题，提出了一种数据增强方法。我们通过在向量量化变分自编码器（VQ-VAE）的潜在空间中进行插值并通过采样生成新样本来解决此问题。训练后的最终分类器、普通医生和ECG专家在一个由1000个样本组成的测试集上评估诊断性能。
相对于未增强的数据，F1分数提高了0 - 6%。与ECG专家相比，深度神经网络在大多数类别中获得了更高的F1分数和灵敏度。
我们的方法可以提高阳性样本不足的ECG数据的分类性能，并达到ECG专家的水平。这种方法可以为普通临床医生提供专业参考意见，减少ECG专家的误诊。
