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Due to the multiple imperfections during the signal acquisition,Electrocardiogram (ECG) datasets are typically contaminated with numerous typesof noise, like salt and pepper and baseline drift. These datasets may containdifferent recordings with various types of noise [1] and thus, denoising maynot be the easiest task. Furthermore, usually, the number of labeledbio-signals is very limited for a proper classification task.

The Effect of Various Strengths of Noises and Data Augmentations on Classification of Short Single-Lead ECG Signals Using Deep Neural Networks