MSECG: Incorporating Mamba for Robust and Efficient ECG Super-Resolution

TL;DR

This paper introduces MSECG, a neural network model that combines recurrent and convolutional layers to improve ECG super-resolution, especially in noisy conditions, while reducing model complexity for better long-term monitoring.

Contribution

MSECG is a novel ECG super-resolution model that integrates Mamba and convolutional layers, achieving superior performance with fewer parameters compared to existing methods.

Findings

MSECG outperforms existing ECG SR models in both clean and noisy environments.

MSECG uses fewer parameters, making it suitable for wearable devices.

The model effectively reconstructs high-resolution ECG signals from low-resolution inputs.

Abstract

Electrocardiogram (ECG) signals play a crucial role in diagnosing cardiovascular diseases. To reduce power consumption in wearable or portable devices used for long-term ECG monitoring, super-resolution (SR) techniques have been developed, enabling these devices to collect and transmit signals at a lower sampling rate. In this study, we propose MSECG, a compact neural network model designed for ECG SR. MSECG combines the strength of the recurrent Mamba model with convolutional layers to capture both local and global dependencies in ECG waveforms, allowing for the effective reconstruction of high-resolution signals. We also assess the model's performance in real-world noisy conditions by utilizing ECG data from the PTB-XL database and noise data from the MIT-BIH Noise Stress Test Database. Experimental results show that MSECG outperforms two contemporary ECG SR models under both clean and noisy conditions while using fewer parameters, offering a more powerful and robust solution for long-term ECG monitoring applications.