  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.
