用分层变分自编码器生成带病理性标记的高分辨率心电图。
Conditional Electrocardiogram Generation Using Hierarchical Variational Autoencoders
- 采用分层VAE架构，实现多病理性心电图条件生成。
- 在下游任务中使AUROC提升最高达2%，优于同类GAN模型。
- 适合需要合成医疗数据的研究者与临床算法开发者。
心血管疾病是全球导致死亡的主要且持续上升的原因。心电图分析是诊断该类疾病的关键环节，通常基于10秒标准12导联心电图。机器学习可提升心电图诊断的速度与准确率，但训练数据不足是主要障碍。受制于医疗数据的成本、标注模糊、类别不平衡及隐私问题，依赖特定病理生成合成样本成为突破方案。现有方法多基于生成对抗网络（GAN），少数研究尝试变分自编码器（VAE），近期表现已接近GAN。本文提出公开可用的条件型新变分自编码器心电图生成模型（cNVAE-ECG），可生成具有多种病理性特征的高分辨率心电图。我们在多个实际下游任务中进行了广泛对比，包括迁移学习场景，结果显示其在受试者工作特征曲线下面积（AUROC）上最高提升2%，优于类似GAN的模型。
原文摘要 · Abstract (English)
Cardiovascular diseases (CVDs) are disorders impacting the heart and circulatory system. These disorders are the foremost and continuously escalating cause of mortality worldwide. One of the main tasks when working with CVDs is analyzing and identifying pathologies on a 12-lead electrocardiogram (ECG) with a standard 10-second duration. Using machine learning (ML) in automatic ECG analysis increases CVD diagnostics' availability, speed, and accuracy. However, the most significant difficulty in developing ML models is obtaining a sufficient training dataset. Due to the limitations of medical data usage, such as expensiveness, errors, the ambiguity of labels, imbalance of classes, and privacy issues, utilizing synthetic samples depending on specific pathologies bypasses these restrictions and improves algorithm quality. Existing solutions for the conditional generation of ECG signals are mainly built on Generative Adversarial Networks (GANs), and only a few papers consider the architectures based on Variational Autoencoders (VAEs), showing comparable results in recent works. This paper proposes the publicly available conditional Nouveau VAE model for ECG signal generation (cNVAE-ECG), which produces high-resolution ECGs with multiple pathologies. We provide an extensive comparison of the proposed model on various practical downstream tasks, including transfer learning scenarios showing an area under the receiver operating characteristic (AUROC) increase up to 2% surpassing GAN-like competitors.
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