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

TL;DR

This paper investigates how different noise levels and data augmentation techniques affect the accuracy of deep neural networks in classifying short single-lead ECG signals, addressing challenges of noise contamination and limited labeled data.

Contribution

It explores the impact of various noise types and data augmentation methods on ECG classification performance using deep learning models.

Findings

Noise significantly affects classification accuracy

Data augmentation improves model robustness

Certain noise types are more challenging for models

Abstract

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.