CARMEN packs ten cardiorespiratory waveforms into one encoder, beating task-specific models on most of 19 tasks with frozen weights
A team at Seoul National University Hospital released CARMEN, a single encoder pretrained without labels on ten cardiorespiratory waveforms totaling about 2.27 million modality-hours, which accepts any combination of co-recorded signals and inputs from 10 s to 5 min. With the encoder frozen and only a linear layer trained, it was significantly better than end-to-end supervised models on 12 of the 15 tasks that had supervised comparators, and better than three pretrained foundation models on 14 to 17 of 19 tasks, with none exceeding it.