提出能量守恒理论，让模型区分生理信号的正常波动与真实概念漂移。
When Should a Model Change Its Mind? An Energy-Based Theory and Regularizer for Concept Drift in Electrocardiogram (ECG) Signals
- 基于信号能量变化约束隐空间移动，判断是否发生真正概念漂移。
- 在多模态心电图上，扰动准确率从72.6%提升至85.5%，融合表示漂移减少45%以上。
- 轻量正则化无需修改架构，适合实时生理信号分析场景。
运行于动态生理信号上的模型需区分良性、标签不变的波动与真实概念漂移。现有概念漂移框架多基于分布变化，无法提供模型内部表征在信号能量发生生理合理波动时可允许的移动范围。因此，深度模型常将振幅、速率或形态的无害变化误判为概念漂移，导致预测不稳定，尤其在多模态融合中。本文提出生理能量守恒理论（PECT），一种面向动态信号的概念稳定性能量基础框架。PECT认为，在虚拟漂移下，归一化隐空间位移应与归一化信号能量变化成比例；持续违反此比例即指示真实概念漂移。我们通过能量约束表示学习（ECRL）实现该原则，一种轻量级正则化器，惩罚能量不一致的隐空间移动，无需修改编码器结构或增加推理开销。尽管PECT适用于一般动态信号，本文在七种单模态与混合模型上对多模态心电图进行实例化与评估。实验表明，在最强的三模态混合模型（1D+2D+Transformer）中，干净准确率基本保持（96.0% → 94.1%），而扰动准确率显著提升（72.6% → 85.5%），融合表示漂移减少超45%。各架构均呈现相似趋势，实证支持PECT作为连续生理信号中概念稳定性的能量-漂移定律。
原文摘要 · Abstract (English)
Models operating on dynamic physiologic signals must distinguish benign, label-preserving variability from true concept change. Existing concept-drift frameworks are largely distributional and provide no principled guidance on how much a model's internal representation may move when the underlying signal undergoes physiologically plausible fluctuations in energy. As a result, deep models often misinterpret harmless changes in amplitude, rate, or morphology as concept drift, yielding unstable predictions, particularly in multimodal fusion settings. This study introduces Physiologic Energy Conservation Theory (PECT), an energy-based framework for concept stability in dynamic signals. PECT posits that under virtual drift, normalized latent displacement should scale proportionally with normalized signal energy change, while persistent violations of this proportionality indicate real concept drift. We operationalize this principle through Energy-Constrained Representation Learning (ECRL), a lightweight regularizer that penalizes energy-inconsistent latent movement without modifying encoder architectures or adding inference-time cost. Although PECT is formulated for dynamic signals in general, we instantiate and evaluate it on multimodal ECG across seven unimodal and hybrid models. Experiments show that in the strongest trimodal hybrid (1D+2D+Transformer), clean accuracy is largely preserved (96.0% to 94.1%), while perturbed accuracy improves substantially (72.6% to 85.5%) and fused representation drift decreases by over 45%. Similar trends are observed across all architectures, providing empirical evidence that PECT functions as an energy-drift law governing concept stability in continuous physiologic signals.
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