DiffCNBP: Lightweight Diffusion Model for IoMT-Based Continuous Cuffless Blood Pressure Waveform Monitoring Using PPG

Continuous monitoring of blood pressure (BP) waveform is challenging in clinical applications due to the invasive nature of traditional techniques. As a result, there is a growing focus on the estimation of continuous BP waveforms from photoplethysmography (PPG) signals obtained through affordable wearable Internet of Medical Things (IoMT) devices. To address this demand, we introduce diffusion for continuous noninvasive BP (DiffCNBP), a lightweight model that employs a joint optimization approach incorporating sequence learning, diffusion modeling, and conditional embedding. The sequence learning module comprises stacked Transformer encoders to capture the temporal information of the PPG signals. These dynamically related features are then fed into a lightweight diffusion module based on structured state-space sequence encoders to learn detailed variation features associated with vascular dynamics. Additionally, the conditional embedding module introduces constraints to incorporate physiologically specific prior information for model estimation, thereby enhancing the fidelity of the estimated BP waveform. Our proposed method was validated using subject-wise fivefold cross-validation on a multisource database of 1415 subjects. This database included data from intensive care unit IoMT applications, where finger-based PPG sensors were employed alongside invasive BP sensors. Experimental results demonstrate that DiffCNBP outperforms other state-of-the-art methods with an average root-mean-square error of 4.36 mmHg for waveform estimation. The mean error ± standard deviation error of systolic and diastolic BP was
$0.67~\pm ~4.29$
mmHg and
$0.37~\pm ~2.46$
mmHg, respectively, meeting the clinical standards. Furthermore, we demonstrated the robust long-term trend-tracking ability of DiffCNBP on resource-constrained devices, indicating its potential IoMT deployment in clinical settings.