Set-Prediction-Based J-Peak Detection for Pillow-Based Ballistocardiography
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866915839388680192 |
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| author | Guo, Shengwei Sun, Guobing |
| author_facet | Guo, Shengwei Sun, Guobing |
| contents | J-peak detection in ballistocardiography (BCG) is a key component of unobtrusive heart rate monitoring during sleep.
Most existing approaches formulate this task as a dense time-point segmentation problem and rely on heuristic post-processing to convert continuous responses into discrete peak events, resulting in redundant model structures and sensitivity to parameter settings.
In this work, we construct and publicly release a pillow-based BCG--ECG dataset consisting of multi-subject, multi-night natural sleep recordings with manually annotated BCG J-peaks.
Based on this dataset, we propose a set-prediction-based J-peak detection framework that directly models peaks as discrete temporal events, eliminating the need for high-resolution segmentation heads and explicit peak suppression.
Experimental results show that, under a shared convolutional backbone, the proposed method achieves superior detection performance compared to a U-Net-based segmentation baseline, while substantially reducing model parameters and computational complexity.
These results indicate that event-level set prediction provides a concise and efficient modeling paradigm for BCG J-peak detection in sleep monitoring. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06221 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Set-Prediction-Based J-Peak Detection for Pillow-Based Ballistocardiography Guo, Shengwei Sun, Guobing Signal Processing J-peak detection in ballistocardiography (BCG) is a key component of unobtrusive heart rate monitoring during sleep. Most existing approaches formulate this task as a dense time-point segmentation problem and rely on heuristic post-processing to convert continuous responses into discrete peak events, resulting in redundant model structures and sensitivity to parameter settings. In this work, we construct and publicly release a pillow-based BCG--ECG dataset consisting of multi-subject, multi-night natural sleep recordings with manually annotated BCG J-peaks. Based on this dataset, we propose a set-prediction-based J-peak detection framework that directly models peaks as discrete temporal events, eliminating the need for high-resolution segmentation heads and explicit peak suppression. Experimental results show that, under a shared convolutional backbone, the proposed method achieves superior detection performance compared to a U-Net-based segmentation baseline, while substantially reducing model parameters and computational complexity. These results indicate that event-level set prediction provides a concise and efficient modeling paradigm for BCG J-peak detection in sleep monitoring. |
| title | Set-Prediction-Based J-Peak Detection for Pillow-Based Ballistocardiography |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2603.06221 |