论文标题
噪声和数据增强的各种优势对使用深神经网络对短的单铅ECG信号分类的影响
The Effect of Various Strengths of Noises and Data Augmentations on Classification of Short Single-Lead ECG Signals Using Deep Neural Networks
论文作者
论文摘要
由于信号采集期间的多个缺陷，心电图（ECG）数据集通常被多种类型的噪声污染，例如盐和胡椒和基线漂移。这些数据集可能包含具有不同类型的噪声[1]的不同记录[1]，因此，DeNoising可能不是最简单的任务。此外，通常，标记的生物信号的数量非常有限，用于适当的分类任务。
Due to the multiple imperfections during the signal acquisition, Electrocardiogram (ECG) datasets are typically contaminated with numerous types of noise, like salt and pepper and baseline drift. These datasets may contain different recordings with various types of noise [1] and thus, denoising may not be the easiest task. Furthermore, usually, the number of labeled bio-signals is very limited for a proper classification task.
