Kuznetsov V V, Moskalenko V A, Gribanov D V, Zolotykh Nikolai Yu
Institute of Information Technologies, Mathematics, and Mechanics, Lobachevsky State University of Nizhni Novgorod, Nizhni Novgorod, Russia.
Mathematics of Future Technologies Center, Lobachevsky State University of Nizhni Novgorod, Nizhni Novgorod, Russia.
Front Genet. 2021 Apr 1;12:638191. doi: 10.3389/fgene.2021.638191. eCollection 2021.
We propose a method for generating an electrocardiogram (ECG) signal for one cardiac cycle using a variational autoencoder. Our goal was to encode the original ECG signal using as few features as possible. 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, 3.83 × 10, indicates good quality of ECG generation too. The extracted new features will help to improve the quality of automatic diagnostics of cardiovascular diseases. Generating new synthetic ECGs will allow us to solve the issue of the lack of labeled ECG for using them in supervised learning.
我们提出了一种使用变分自编码器生成一个心动周期心电图（ECG）信号的方法。我们的目标是使用尽可能少的特征对原始ECG信号进行编码。使用这种方法，我们提取了一个包含25个新特征的向量，在许多情况下这些特征是可以解释的。生成的ECG具有相当自然的外观。最大均值差异度量值较低，为3.83×10，这也表明ECG生成的质量良好。提取的新特征将有助于提高心血管疾病自动诊断的质量。生成新的合成ECG将使我们能够解决在监督学习中使用时缺乏标记ECG的问题。
