AI 中文总结
该研究提出等相位自监督目标与无额外参数的Winder架构，在PTB-XL数据集上以约1M参数实现与SOTA自监督方法相当的诊断准确率，验证了编码心脏相位对称性的有效性。
AI 中文摘要
生理过程的循环结构为自监督表征学习提供了天然先验，心脏周期则是利用该先验的特别明确的场景。我们推导了等相位自监督目标，并引入Winder，一种联合嵌入架构，其将表征组织为相位不变坐标与相位旋转谐波子空间。其传输算子固定且为闭式形式，源自周期几何而非学习得到，且不增加参数。在PTB-XL数据集上采用冻结线性探针协议评估，Winder在约100万参数规模下达到了最先进自监督方法报告的诊断准确率范围，同时表现出等相位潜在几何。这些发现表明，显式编码心脏相位对称性可保留诊断有用信息，同时生成清晰、参数高效且与可测量生理量直接关联的潜在几何。
英文摘要
The cyclic structure of physiological processes offers a natural prior for self-supervised representation learning, and the cardiac cycle provides a particularly well-defined setting in which to exploit it. We derive a phase-equivariant self-supervised objective and introduce Winder, a joint-embedding architecture that organises representations into phase-invariant coordinates and phase-rotating harmonic subspaces. Its transport operator is fixed and closed-form, derived from the cycle's geometry rather than learned, and adds no parameters. Evaluated on PTB-XL under a frozen linear-probe protocol, Winder attains diagnostic accuracy within the range reported by state-of-the-art self-supervised methods at a ~1 M parameter footprint, while exhibiting phase-equivariant latent geometry. These findings demonstrate that explicitly encoding cardiac-phase symmetry can preserve diagnostically useful information while yielding a latent geometry that is legible, parameter-efficient, and directly tied to a measurable physiological quantity.
