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

Autor:	Lin, Jie, Chiu, I, Wang, Kuan-Chen, Liu, Kai-Chun, Wang, Hsin-Min, Yeh, Ping-Cheng, Tsao, Yu
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: 5 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.04861 Zobrazit plný text záznamu View this record from Arxiv