Automatic Detection of Noisy Electrocardiogram Signals without Explicit Noise Labels

TL;DR

This paper introduces a two-stage deep learning framework for automatic detection of noisy ECG signals, improving diagnostic accuracy by identifying contaminated samples without needing explicit noise labels.

Contribution

It proposes a novel deep learning-based method that effectively detects noisy ECG signals across different datasets without explicit noise annotations.

Findings

Effective detection of slightly and highly noisy ECG samples

Successful transferability of the model across datasets

Improved accuracy in ECG noise identification

Abstract

Electrocardiogram (ECG) signals are beneficial in diagnosing cardiovascular diseases, which are one of the leading causes of death. However, they are often contaminated by noise artifacts and affect the automatic and manual diagnosis process. Automatic deep learning-based examination of ECG signals can lead to inaccurate diagnosis, and manual analysis involves rejection of noisy ECG samples by clinicians, which might cost extra time. To address this limitation, we present a two-stage deep learning-based framework to automatically detect the noisy ECG samples. Through extensive experiments and analysis on two different datasets, we observe that the deep learning-based framework can detect slightly and highly noisy ECG samples effectively. We also study the transfer of the model learned on one dataset to another dataset and observe that the framework effectively detects noisy ECG samples.