Robust J-Peak Detection From Ballistocardiogram Using Diffusion-Based Model

Cardiovascular diseases have long represented one of the most significant global health challenges. Due to its non-contact and non-invasive characteristics, the ballistocardiogram (BCG) shows considerable promise for various applications, particularly in heart rate variability (HRV) monitoring. Accurate J-peak detection is a critical prerequisite for effective BCG-based analysis. However, BCG signal quality is affected by multiple factors—including sensor type, measurement posture, and individual differences—posing challenges to reliable J-peak detection. In this paper, a diffusion-based J-peak detection method, named J-Diff model, is proposed. To capture the underlying data distribution and address uncertainties in feature variations, a discriminative model termed the conditional detector is employed to generate initial segmentation results. These results are subsequently refined by a conditional diffusion probabilistic model (CDDPM)-based diffusion refiner that models the data distribution across varying noise levels, thereby yielding more accurate and robust segmentation outcomes. Specifically, the conditional cross transformer (CCT) is introduced to integrate features from the condition detector and the diffusion refiner, thereby reducing the domain gap and directing the network’s attention toward J-peaks. The experimental evaluations were conducted on two BCG datasets collected under different postures and using different sensor types, achieving average Precision of 88.02% and 97.32%, Recall of 86.79% and 97.10%, and F1-scores of 0.874 and 0.972, respectively. Correlation analysis revealed significantly high correlations between all HRV indices derived from BCG and those obtained from ECG. These results highlight the effectiveness of applying the proposed J-Diff model in improving the reliability and generalization of BCG measurement systems.