Dense and Query-Set Prediction for J-Peak Detection in Pillow-Based Ballistocardiography

Abstract

Pillow-based ballistocardiography (BCG) enables unobtrusive cardiac monitoring, but J-peak detection is commonly learned as dense sequence labeling although the desired output is a sparse event set. This letter asks a narrower question: how do dense and query-set outputs differ when the data, encoder, validation protocol, and event evaluator are controlled? We formulate one-dimensional point-set prediction with 64 learned queries and Hungarian assignment, and compare it with a shared-backbone dense Transformer and a U-Net--BiLSTM. Evaluation uses five-subject leave-one-subject-out testing, three independently seeded runs, validation-only postprocessing selection, and strict one-to-one peak association. The dense Transformer attains the highest subject-wise pooled F1 ($0.786\pm 0.105$) and precision ($0.817\pm 0.089$), whereas U-Net--BiLSTM obtains $0.780\pm 0.094$ F1. Set+DN reaches $0.764\pm 0.112$ F1 but the lowest beat-count error ($0.862\pm 0.584$ beats/epoch), compared with $2.790\pm 1.356$ for the dense Transformer. DN changes set-model F1 by only $+0.004$. The results identify distinct event-accuracy and count-fidelity operating points; they do not establish universal superiority of either output formulation.