ArterialNet: Arterial Blood Pressure Reconstruction

Sicong Huang; Roozbeh Jafari; Bobak Jack Mortazavi

Abstract

Accurate and continuous monitoring of arterial blood pressure (ABP) is vital for clinical hemodynamic monitoring. However, current methods are either invasive, requiring insertion of catheters, or provide limited information, lacking comprehensive ABP waveforms. Cuffless wearable solutions, combined with deep learning, offer potential but face challenges in accurately reconstructing ABP waveforms and estimating systolic and diastolic blood pressure (SBP/DBP) due to individual variability. We propose a novel custom pre-trained backbone and a tailored optimization function to address these challenges. Our method demonstrates superior performance in ABP waveform reconstruction and accurate SBP/DBP estimations, while significantly reducing subject variance. To validate the effectiveness of our approach, we conducted comprehensive evaluations using both in-clinic data and a pioneering study involving remote health monitoring with cuffless data. Our results surpass previous efforts, demonstrating a root mean square error (RMSE) of 5.41 ± 1.35 mmHg and a minimum of 58% lower standard deviation (SD) across all measurements. These outcomes highlight the robustness and precision of our method in accurately estimating SBP/DBP and reconstructing ABP waveforms. Furthermore, we assessed the performance of our solution in non-clinical settings using the CTRAL BioZ dataset. The evaluation yielded an RMSE of 8.66 ± 1.13 mmHg for ABP, proving the potential of ABP reconstruction under remote health settings.

Keywords: Cuffless Blood Pressure; Arterial Blood Pressure Waveform; photoplethysmography (PPG); Remote Health

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