Background and Objective

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.

Methods

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.

Results

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.

Conclusions

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.

中文翻译：

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使用VQ-VAE改善对短时12导联心电图记录中异常的识别。

背景与目的

形态学诊断是短期12导联心电图（ECG）的基本临床任务。由于缺乏阳性样本和其他因素，目前尚无可比拟的专家进行心电图形态学识别的算法。我们的目标是开发一种ECG专家级深度学习方法，该方法可以准确识别真实场景数据中的十个ECG形态异常。

方法

我们建立了一个短时的12导联心电图图像数据集，该数据集由大约200,000个样本组成。为了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）的潜在空间内插并通过采样生成新样本来解决该问题。经过培训的最终分类器，一般医生和ECG专家在由1000个样本组成的测试集中评估了诊断性能。

结果

相对于未扩充的数据，F1得分提高了0–6％。与ECG专家相比，深度神经网络在大多数类别中均获得了更高的F1分数和灵敏度。

结论

我们的方法可以在阳性样本不足的情况下提高心电图数据的分类性能，并达到心电图专家的水平。这种方法可以为普通临床医生提供专业的参考意见，并减少ECG专家的错误。