The Effect of Various Strengths of Noises and Data Augmentations on Classification of Short Single-Lead ECG Signals Using Deep Neural Networks
This work tackles the problem of noisy and limited ECG data for medical diagnosis, but appears incremental as it focuses on evaluating existing methods rather than introducing new ones.
The paper addresses the challenge of classifying short single-lead ECG signals by investigating the impact of different noise types and data augmentations on deep neural network performance, but does not report specific numerical results.
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.