论文标题
心电图生成和特征提取使用变量自动编码器
Electrocardiogram Generation and Feature Extraction Using a Variational Autoencoder
论文作者
论文摘要
我们提出了一种使用变异自动编码器生成一个心脏周期的心电图信号（ECG）信号的方法。使用此方法，我们提取了一个新的25个功能的向量，在许多情况下可以解释。产生的心电图具有相当自然的外观。最大平均差异度量的低值，0.00383，也表明ECG的质量也很高。提取的新功能将有助于提高心血管疾病的自动诊断质量。此外，生成新的合成性心电图将使我们能够解决缺乏标记的ECG用于监督学习中的问题。
We propose a method for generating an electrocardiogram (ECG) signal for one cardiac cycle using a variational autoencoder. Using this method we extracted a vector of new 25 features, which in many cases can be interpreted. The generated ECG has quite natural appearance. The low value of the Maximum Mean Discrepancy metric, 0.00383, indicates good quality of ECG generation too. The extracted new features will help to improve the quality of automatic diagnostics of cardiovascular diseases. Also, generating new synthetic ECGs will allow us to solve the issue of the lack of labeled ECG for use them in supervised learning.
