ECG Signal Denoising Using Multi-scale Patch Embedding and Transformers

TL;DR

This paper introduces a novel deep learning approach combining multi-scale convolutional embeddings with transformer architecture to effectively denoise ECG signals, improving the accuracy of cardiovascular monitoring.

Contribution

It presents a new method that integrates multi-scale patch embedding with transformers specifically for ECG signal denoising, enhancing noise removal performance.

Findings

Improved signal-to-noise ratio in ECG signals.

Effective noise reduction across various noise levels.

Enhanced downstream cardiovascular analysis accuracy.

Abstract

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.