MSECG: Incorporating Mamba for Robust and Efficient ECG Super-Resolution
Jie Lin, I Chiu, Kuan-Chen Wang, Kai-Chun Liu, Hsin-Min Wang, Ping-Cheng Yeh, Yu Tsao
TL;DR
This work tackles the challenge of power-efficient long-term ECG monitoring by reconstructing high-resolution ECG signals from low-rate inputs. It introduces MSECG, a compact model that fuses Mamba-based bidirectional recurrent blocks with convolutional layers, employing 1D pixel shuffle upsampling and a skip connection to enhance reconstruction. Across the PTB-XL and NSTDB datasets, MSECG outperforms state-of-the-art CNN-based SR methods and does so with fewer parameters, maintaining robustness under realistic noise. The study demonstrates the practical potential of MSECG for robust, energy-efficient ECG surveillance and lays groundwork for integrating SR with downstream cardiac analysis tasks.
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.