Automatic Detection of Noisy Electrocardiogram Signals without Explicit Noise Labels
This addresses the issue of inaccurate automated diagnosis and time-consuming manual rejection for clinicians in cardiovascular disease detection, though it is incremental as it builds on existing deep learning methods.
The paper tackles the problem of noisy ECG signals interfering with diagnosis by presenting a two-stage deep learning framework to automatically detect such noise without explicit labels, showing effective detection of slightly and highly noisy samples across two datasets.
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.