用Mamba+卷积实现低功耗心电图超分辨率，更准更快更省资源
MSECG: Incorporating Mamba for Robust and Efficient ECG Super-Resolution
- 融合Mamba与卷积层，同时捕捉心电信号局部和全局特征
- 在清洁和噪声环境下均优于现有模型，参数量更少
- 适合长期可穿戴设备的心电监测，抗干扰能力强
心电图（ECG）信号在心血管疾病诊断中至关重要。为降低可穿戴或便携式设备在长期心电监测中的功耗，超分辨率（SR）技术被提出，使设备可在低采样率下采集并传输信号。本文提出MSECG，一种用于心电图超分辨率的紧凑型神经网络模型。MSECG结合循环Mamba模型与卷积层，有效捕捉心电信号波形中的局部与全局依赖关系，实现高质量高分辨率信号重建。我们通过使用PTB-XL数据库的ECG数据及MIT-BIH Noise Stress Test Database的噪声数据，在真实噪声条件下评估了模型性能。实验结果表明，MSECG在清洁与噪声环境下均优于两种现有先进心电图超分辨率模型，且参数量更少，为长期心电监测应用提供了更高效、更鲁棒的解决方案。
原文摘要 · Abstract (English)
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.
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