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Beat-to-Beat Continuous Blood Pressure Estimation Using Bidirectional Long Short-Term Memory Network.

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  • 1Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 03080, Korea.

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Summary

Continuous blood pressure monitoring is crucial for hypertension management. This study proposes a cuffless deep learning model using ECG, PPG, and BCG, achieving accurate estimations for both one-day and multi-day tests.

Keywords:
ballistocardiogramcuffless blood pressuregeneral blood pressure estimationlong short-term memory

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Cardiovascular Monitoring

Background:

  • Continuous blood pressure (BP) monitoring is vital for hypertension management, but traditional cuff-based methods are inconvenient.
  • Existing cuffless BP estimation models often require extensive training data and lack generalizability.
  • Deep learning approaches offer potential for accurate and non-invasive BP estimation.

Discussion:

  • This study introduces a novel BP estimation model utilizing a bidirectional long short-term memory (BiLSTM) network.
  • Feature extraction from electrocardiogram (ECG), photoplethysmogram (PPG), and ballistocardiogram (BCG) signals enhances model performance.
  • The leave-one-subject-out (LOSO) cross-validation strategy ensures model generalizability across diverse subjects.

Key Insights:

  • The proposed BiLSTM model achieved a mean absolute error (MAE) of 2.56 mmHg for systolic BP (SBP) and 2.05 mmHg for diastolic BP (DBP) in one-day tests.
  • The LOSO method combined with fine-tuning demonstrated superior compatibility for multi-day BP monitoring scenarios.
  • Multi-day tests yielded MAE values of 5.82 mmHg for SBP and 5.24 mmHg for DBP, indicating reliable performance over time.

Outlook:

  • Further research can explore incorporating additional physiological signals to improve multi-day BP estimation accuracy.
  • The developed model holds promise for developing wearable, cuffless BP monitoring devices for continuous health tracking.
  • Clinical validation and large-scale studies are essential to translate this technology into widespread clinical practice.