ECG Signal Denoising Using Multi-scale Patch Embedding and Transformers
This addresses noise contamination in ECG signals for cardiovascular disease monitoring, but appears incremental as it combines existing techniques.
The paper tackled the problem of ECG signal denoising, which is crucial for cardiovascular monitoring due to noise interference, by proposing a deep learning method combining a one-dimensional convolutional layer with transformer architecture, resulting in enhanced denoising capability.
Cardiovascular disease is a major life-threatening condition that is commonly monitored using electrocardiogram (ECG) signals. However, these signals are often contaminated by various types of noise at different intensities, significantly interfering with downstream tasks. Therefore, denoising ECG signals and increasing the signal-to-noise ratio is crucial for cardiovascular monitoring. In this paper, we propose a deep learning method that combines a one-dimensional convolutional layer with transformer architecture for denoising ECG signals. The convolutional layer processes the ECG signal by various kernel/patch sizes and generates an embedding called multi-scale patch embedding. The embedding then is used as the input of a transformer network and enhances the capability of the transformer for denoising the ECG signal.