发表机构
ETH Zurich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对长程心电图去噪中卷积感受野受限和Transformer二次复杂度问题，提出在卷积瓶颈插入选择性状态空间块的Mamba增强模型，以线性复杂度实现长程建模，在重建保真度、噪声鲁棒性和下游分类上取得最优性能。
AI 中文摘要
心电图（ECG）记录会受到非平稳噪声源的污染，这些噪声源会降低诊断可靠性，尤其是在动态和长时间记录中。现有的深度学习去噪器存在局限性：卷积架构受限于感受野，基于Transformer的模型随序列长度呈二次方扩展，而基于扩散的方法则带来高昂的推理成本。我们提出了一种Mamba增强模型，在卷积瓶颈处插入选择性状态空间块，以线性复杂度将局部特征提取与长程时间建模相结合。我们综合评估了所提出的模型在重建保真度、噪声鲁棒性、记录长度扩展以及跨40多个病理类别的下游诊断分类方面的性能。在合成和真实数据集上，我们的模型实现了最高的信噪比（SNR）和最低的均方根误差（RMSE），且Mamba的优势随着序列长度的增加和在低信噪比（SNR）区间内而增强。在使用两个独立分类器进行分类时，所提出的基于Mamba的模型在所有去噪器中取得了最佳的宏AUROC，并优于其卷积基础模型。校准结果更为细致且依赖于分类器：去噪在Inception1D上改善了二元交叉熵和Brier分数，但通常无法在ResNet1D-Wang上超越噪声输入，而特定导联的Mamba变体是唯一能在两个分类器上均改善两种校准指标（相对于噪声基线）的去噪器。逐类分析揭示了形态学依赖的益处：Mamba显著改善了ST/T改变诊断，这些诊断依赖于宽泛且上下文敏感的波形。
英文摘要
Electrocardiogram (ECG) recordings are corrupted by non-stationary noise sources that degrade diagnostic reliability, particularly in ambulatory and long-duration recordings. Deep learning denoisers exist, but convolutional architectures are limited by their receptive field, transformer-based models scale quadratically with sequence length, and diffusion-based approaches incur prohibitive inference cost. We propose a Mamba-augmented model that inserts selective state-space blocks at the convolutional bottleneck, combining local feature extraction with long-range temporal modeling at linear complexity. We comprehensively evaluate the proposed model with respect to reconstruction fidelity, noise robustness, recording-length scaling, and downstream diagnostic classification across over 40 pathology classes. On synthetic and real datasets, our model achieves the highest SNR and lowest RMSE, with the Mamba advantage increasing with sequence length and in low-SNR regimes. On classification with two independent classifiers, the proposed Mamba-based models achieve the best macro AUROC among all denoisers and improve over their convolutional base models. Calibration is more nuanced and classifier-dependent: denoising improves Binary Cross-Entropy and Brier score on Inception1D but often fails to beat the noisy input on ResNet1D-Wang, and the lead-specific Mamba variant is the only denoiser to improve both calibration metrics over the noisy baseline on both classifiers. Per-class analysis reveals a morphology-dependent benefit: Mamba substantially improves ST/T-change diagnoses, which depend on broad, context-sensitive waveforms.
CommentsFirst three authors are co-first. Last two authors are co-last
