S2M2ECG：基于时空双向状态空间模型的多分支Mamba用于心电图研究
S2M2ECG: Spatio-temporal bi-directional State Space Model Enabled Multi-branch Mamba for ECG
AI总结：
针对多导联心电图多源特征融合与性能、复杂度难平衡的问题，提出具备三级融合机制的多分支轻量状态空间模型S2M2ECG，在多场景实现高性能且参数量极低，为心血管病诊断提供高效方案。
摘要：
作为心血管疾病（CVD）诊断最有效的方法之一，多导联心电图（ECG）信号带来了典型的多传感器信息融合挑战，这一问题一直是深度学习领域的研究热点。尽管已有大量采用不同深度学习架构的算法被提出，但在性能、计算复杂度和多源ECG特征融合之间保持平衡仍然颇具挑战。近年来，状态空间模型（SSMs），尤其是Mamba，在多个领域展现出了显著的有效性。其固有的高效计算设计和线性复杂度，使其特别适用于ECG这类低维数据。本研究提出了S2M2ECG，这是一种具备三级融合机制的SSM架构：（1）采用分段令牌化的时空双向SSMs，用于低层信号融合；（2）结合双向扫描的导联内时序信息融合，以提升前向和后向两个方向的识别准确率；（3）用于空间信息融合的导联间特征交互模块。为充分利用ECG信号固有的多导联特性，该架构融入了多分支设计和导联融合模块，既能对每个导联进行单独分析，又能确保各导联间的无缝整合。实验结果表明，S2M2ECG在节律、形态学和临床场景中均取得了优异性能。此外，其轻量级架构使其在现有模型中参数数量几乎最少，非常适合高效推理和便捷部署。总体而言，S2M2ECG提供了一种极具前景的替代方案，在性能、计算复杂度和ECG专属特性之间实现了出色的平衡，为CVD诊断领域的高性能轻量计算铺平了道路。
英文摘要：
As one of the most effective methods for cardiovascular disease (CVD) diagnosis, multi-lead Electrocardiogram (ECG) signals present a characteristic multi-sensor information fusion challenge that has been continuously researched in deep learning domains. Despite the numerous algorithms proposed with different DL architectures, maintaining a balance among performance, computational complexity, and multi-source ECG feature fusion remains challenging. Recently, state space models (SSMs), particularly Mamba, have demonstrated remarkable effectiveness across various fields. Their inherent design for high-efficiency computation and linear complexity makes them particularly suitable for low-dimensional data like ECGs. This work proposes S2M2ECG, an SSM architecture featuring three-level fusion mechanisms: (1) Spatio-temporal bi-directional SSMs with segment tokenization for low-level signal fusion, (2) Intra-lead temporal information fusion with bi-directional scanning to enhance recognition accuracy in both forward and backward directions, (3) Cross-lead feature interaction modules for spatial information fusion. To fully leverage the ECG-specific multi-lead mechanisms inherent in ECG signals, a multi-branch design and lead fusion modules are incorporated, enabling individual analysis of each lead while ensuring seamless integration with others. Experimental results reveal that S2M2ECG achieves superior performance in the rhythmic, morphological, and clinical scenarios. Moreover, its lightweight architecture ensures it has nearly the fewest parameters among existing models, making it highly suitable for efficient inference and convenient deployment. Collectively, S2M2ECG offers a promising alternative that strikes an excellent balance among performance, computational complexity, and ECG-specific characteristics, paving the way for high-performance, lightweight computations in CVD diagnosis.
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