用Mamba架构提升心电图去基线漂移效率与效果
MECG-E: Mamba-based ECG Enhancer for Baseline Wander Removal
- 采用Mamba架构实现快速高效的心电图去噪
- 在多种噪声条件下优于现有模型，推理时间更短
- 适合实时心电监测系统，尤其对低延迟要求高
心电图（ECG）是诊断心血管疾病的重要无创手段，但易受电气干扰或基线漂移等噪声影响，降低诊断准确性。现有去噪方法在极强噪声下表现不佳，或需多步推理导致在线处理延迟。本文提出基于Mamba架构的新型心电图去噪模型MECG-E，利用Mamba的快速推理能力和强大非线性映射性能。实验表明，MECG-E在不同噪声条件下均超越多个知名模型，在多项指标上表现更优。同时，其推理时间显著低于当前最先进的基于扩散模型的去噪方法，验证了模型的功能性与高效性。
原文摘要 · Abstract (English)
Electrocardiogram (ECG) is an important non-invasive method for diagnosing cardiovascular disease. However, ECG signals are susceptible to noise contamination, such as electrical interference or signal wandering, which reduces diagnostic accuracy. Various ECG denoising methods have been proposed, but most existing methods yield suboptimal performance under very noisy conditions or require several steps during inference, leading to latency during online processing. In this paper, we propose a novel ECG denoising model, namely Mamba-based ECG Enhancer (MECG-E), which leverages the Mamba architecture known for its fast inference and outstanding nonlinear mapping capabilities. Experimental results indicate that MECG-E surpasses several well-known existing models across multiple metrics under different noise conditions. Additionally, MECG-E requires less inference time than state-of-the-art diffusion-based ECG denoisers, demonstrating the model's functionality and efficiency.
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