SelfDenoiser: Self-supervised Seismic Signal Denoiser for Continuous and Contactless Cardiac Monitoring

Cardiovascular diseases (CVDs) remain a major global health challenge, highlighting the urgent need for advanced cardiac monitoring solutions. Continuous, contactless cardiac monitoring using seismic sensors enables comfortable, privacy-preserving assessments by capturing subtle heart vibrations. However, these systems are highly susceptible to diverse noise sources. Existing denoising methods struggle to handle the complex noise in cardiac seismic signals and poorly leverage the abundant unlabeled data. To address these challenges, we propose SelfDenoiser, a self-supervised framework for denoising and reconstructing cardiac seismic signals using unlabeled data. During training, SelfDenoiser first selects clean segments from the unlabeled pool, then injects adaptive noise into each segment to simulate shared, hard-to-remove interference commonly observed in real-world noise distributions. In addition, realistic artifacts are extracted and integrated into clean signals to model high-intensity, abrupt noise events. An encoder-decoder network designed with fixed temporal resolution is subsequently trained to recover the clean signals, guided by a loss function that captures both temporal and spectral characteristics. We evaluated SelfDenoiser on 11,392 hours of data collected in an Intensive Care Unit (ICU) using seismic sensor-based systems. The model was trained on 610 hours of clean signals selected from a 5176-hour unlabeled pool and tested on a 6216-hour labeled dataset. Results showed substantial improvements in two downstream tasks: heart rate (HR) and inter-beat interval (IBI) estimation, with notably increased data utilization and better accuracy compared to conventional denoising methods. This highlights SelfDenoiser's capability to transform low-quality, noisy signals into high-fidelity, reliable cardiac data.