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arXiv Machine Learning By Shusaku Maeda, Masahiro Nakano, Tomoharu Iwata, Kenji Komiya, Ryo Nishikimi, Kunio Kashino

Impact of Multiple Non-Invasive Biosignals on Cardiovascular Biomarker Estimation via Simulation-Based Inference

Read the original on arXiv Machine Learning →

The study investigates how adding ballistocardiography (BCG) signals to photoplethysmography (PPG) and arterial pressure wave (APW) signals affects the estimation of cardiovascular biomarkers. Using a unified whole‑body cardiovascular model, synthetic PPG, APW, and BCG signals were generated and analyzed with neural posterior estimation and simulation‑based inference. Results show that incorporating BCG significantly improves estimation accuracy, converting multimodal posterior distributions from PPG/APW alone into unimodal distributions, thereby enhancing the reliability of cardiovascular biomarker estimation.

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