Learning to Access Latent Memory for ECG Restoration under Distribution Shift

Electrocardiogram (ECG) denoising is commonly learned through direct reconstruction from corrupted inputs to clean targets, which can couple restoration to training-specific signal-corruption relationships and lead to substantial degradation when signal or corruption characteristics shift at test time. To address this challenge, we treat clean ECGs not only as reconstruction targets, but also as a source of reusable latent structure that can be learned and retained independently of the corrupted-input mapping. We introduce a two-stage framework that first curates this clean structure in a key-value memory through clean-signal reconstruction, and then uses Memory Access Alignment (MAA) to align corrupted observations with the retained memory for reconstruction. During alignment, only the encoder bottleneck and memory router are adapted, while the curated memory and decoder remain frozen. Across multiple ECG datasets and both convolutional and Transformer-based backbones, the proposed framework consistently improves reconstruction under shifts in signal and corruption characteristics. Mechanistic analyses further show that closer agreement with the memory-access patterns induced by clean signals does not necessarily correspond to better reconstruction, while decoder-sensitive readout changes account for most of the improvement or deterioration induced by adaptation. These findings support learning effective access to retained clean structure as a strategy for robust restoration under distribution shift. Our code is available at https://anonymous.4open.science/r/MAA_ECG_Denoising-A0D4/README.md.