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ArterialNet: Reconstructing Arterial Blood Pressure Waveform With Wearable Pulsatile Signals, a Cohort-Aware Approach
Sicong Huang1, Roozbeh Jafari2,3,4,5, Bobak J Mortazavi1
1Department of Computer Science and EngineeringTexas A&M University College Station TX 77840 USA.
Insights
ArterialNet accurately reconstructs continuous arterial blood pressure (ABP) waveforms non-invasively. This AI model improves accuracy in estimating systolic and diastolic blood pressure (SBP/DBP), showing promise for remote health monitoring.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Medicine
- Cardiovascular Monitoring
Background:
- Continuous arterial blood pressure (ABP) monitoring is crucial for hemodynamic assessment but requires invasive procedures.
- Existing non-invasive methods for ABP reconstruction from pulsatile signals often yield inaccurate systolic and diastolic blood pressure (SBP/DBP) estimations and are sensitive to individual variability.
Purpose of the Study:
- To develop a novel deep learning model, ArterialNet, for accurate non-invasive reconstruction of continuous arterial blood pressure (ABP) waveforms.
- To enhance the estimation of SBP/DBP and reduce subject variability compared to existing non-invasive techniques.
- To evaluate ArterialNet's performance and robustness in both clinical and remote health settings.
Main Methods:
- ArterialNet integrates generalized pulsatile-to-ABP signal translation with personalized feature extraction.
- The model employs hybrid loss functions and regularization techniques to optimize performance.
- Model architecture and robustness were assessed through ablation studies on data quality and availability.
Main Results:
- ArterialNet achieved a root mean square error (RMSE) of 5.41 ± 1.35 mmHg on the MIMIC-III dataset, outperforming existing signal translation techniques by 58% in standard deviation.
- In a remote health scenario, ArterialNet reconstructed ABP waveforms with an RMSE of 7.99 ± 1.91 mmHg.
- Ablation studies confirmed the contributions of individual components and the model's robustness.
Conclusions:
- ArterialNet demonstrates superior performance in ABP reconstruction and SBP/DBP estimation with significantly reduced subject variance.
- The model shows substantial potential for reliable hemodynamic monitoring in remote health settings.
- ArterialNet offers a robust and accurate non-invasive solution for continuous blood pressure monitoring.
Abstract:
Goal: Continuous arterial blood pressure (ABP) waveform is invasive but essential for hemodynamic monitoring. Current non-invasive techniques reconstruct ABP waveforms with pulsatile signals but derived inaccurate systolic and diastolic blood pressure (SBP/DBP) and were sensitive to individual variability. Methods: ArterialNet integrates generalized pulsatile-to-ABP signal translation and personalized feature extraction using hybrid loss functions and regularizations. Results: ArterialNet achieved a root mean square error (RMSE) of 5.41 ± 1.35 mmHg on MIMIC-III, achieving 58% lower standard deviation than existing signal translation techniques. ArterialNet also reconstructed ABP with RMSE of 7.99 ± 1.91 mmHg in remote health scenario. Conclusion: ArterialNet achieved superior performance in ABP reconstruction and SBP/DBP estimations with significantly reduced subject variance, demonstrating its potential in remote health settings. We also ablated ArterialNet's architecture to investigate contributions of each component and evaluated ArterialNet's translational impact and robustness by conducting a series of ablations on data quality and availability.
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