发表机构
University of Connecticut（康涅狄格大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出置换不变框架PI-AMFM，用于变基数AM-FM模式分解，结合多尺度编码器、Mamba主干和匈牙利匹配，在合成及真实信号上取得更低误差并捕捉生理动态。
AI 中文摘要
生理记录通常包含非平稳振荡成分，其数量和动态在不同信号间有所变化。调幅-调频（AM-FM）表示非常适合刻画此类动态，并在生物医学信号分析中显示出广泛的实用性。近期方法引入了神经网络以从数据中学习模式分解模式，但成分基数通常是预定义的，或通过单独的停止或选择机制来确定。我们提出了一种用于变基数AM-FM模式分解的置换不变神经框架（PI-AMFM）。PI-AMFM结合了多尺度时间编码器、Mamba主干和成分存在性估计，并在训练过程中采用置换不变的匈牙利匹配。在合成AM-FM信号上，PI-AMFM实现了比对比方法更低的分解误差、瞬时频率误差、重构误差和模式计数误差，同时在交叉啁啾示例中保持了整体轨迹模式。在光电容积描记记录中，尽管仅使用合成信号进行训练，恢复的模式仍捕捉到了心脏和呼吸动态。这些结果支持PI-AMFM用于非平稳生物医学信号变基数分解的可行性。
英文摘要
Physiological recordings often contain nonstationary oscillatory components whose number and dynamics vary across signals. Amplitude- and frequency-modulated (AM-FM) representations are well suited to characterizing such dynamics and have shown broad utility in biomedical signal analysis. Recent approaches have incorporated neural networks to learn mode decomposition patterns from data, but component cardinality is often predefined or determined through separate stopping or selection mechanisms. We propose a permutation-invariant neural framework for variable-cardinality AM-FM mode decomposition (PI-AMFM). PI-AMFM combines a multiscale temporal encoder, Mamba backbone, and component-presence estimation, with permutation-invariant Hungarian matching during training. On synthetic AM-FM signals, PI-AMFM achieved lower decomposition, instantaneous-frequency, reconstruction, and mode-count errors than the compared methods while preserving the overall trajectory pattern in a crossing-chirp example. On photoplethysmographic recordings, recovered modes captured cardiac and respiratory dynamics despite training only on synthetic signals. These results support the feasibility of PI-AMFM for variable-cardinality decomposition of nonstationary biomedical signals.
Comments5 pages, 3 figures
