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 ($0.786\pm0.105$) and precision ($0.817\pm0.089$), whereas U-Net--BiLSTM obtains $0.780\pm0.094$ F1. Set+DN reaches $0.764\pm0.112$ F1 but the lowest beat-count error ($0.862\pm0.584$ beats/epoch), compared with $2.790\pm1.356$ 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.
From: Shengwei Guo [view email]
[v1]
Fri, 6 Mar 2026 12:33:45 UTC (130 KB)
[v2]
Sat, 12 Sep 2026 10:22:27 UTC (15,313 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。