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Paperdoi:10.1109/embc46164.2021.9630319

Recurrent Neural Network Models for Blood Pressure Monitoring Using PPG Morphological Features

Venue listed2021Added Nov 5, 2026

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Authors: Chadi El Hajj, Panayiotis A. Kyriacou

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

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TL;DR

Continuous non-invasive Blood Pressure (BP) monitoring is vital for the early detection and control of hypertension. However, this is yet not possible as all current non-invasive BP devices are cuff-based devices and hence precluding continuous monitoring. Several methods have been proposed to overcome this challenge, one of which utilises the Photoplethysmograph (PPG) signal in an effort to predict reliable BP values from this signal using various computational approaches.

  • Published in 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (2021).

Abstract

Continuous non-invasive Blood Pressure (BP) monitoring is vital for the early detection and control of hypertension. However, this is yet not possible as all current non-invasive BP devices are cuff-based devices and hence precluding continuous monitoring. Several methods have been proposed to overcome this challenge, one of which utilises the Photoplethysmograph (PPG) signal in an effort to predict reliable BP values from this signal using various computational approaches. Although, good performance has been reported in the literature, it was mainly achieved on a small inadequate sample size using conventional models that are unable to account for the temporal variations in the input vector. To address these limitations, this paper proposes cuff-less and continuous blood pressure estimation using Long Short-term Memory (LSTM) and Gated Recurrent Units (GRU). The models were evaluated on 942 patients acquired from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) dataset. The proposed models produced superior results in comparison with conventional artificial neural network. In particular, the best performance was achieved by the GRU, with mean absolute error and standard deviation of 5.77 ± 8.52 mmHg and 3.33±5.02 mmHg for systolic (SBP) and diastolic blood pressure (DBP), respectively. Furthermore, the results comply with the international standards for cuff-less blood pressure estimation.

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Tasks & signals

Tasks

Blood pressure

Signals

PPG

Keywords

Kyriacou BibliographyBlood Pressure Vascular Health

How it works

How Method works: Setup → Method → TargetSetup: Signals (PPG). Method: Method. Target: Blood pressure.SetupSignals: PPGSignalsPPGMethodMethodTargetBlood pressureBlood pressure

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Cite this paper with the fields recorded here. Check it against the publisher before submitting.

Hajj, C. E., & Kyriacou, P. A. (2021). Recurrent Neural Network Models for Blood Pressure Monitoring Using PPG Morphological Features. 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society. https://doi.org/10.1109/embc46164.2021.9630319
Hajj, Chadi El and Panayiotis A. Kyriacou. 2021. "Recurrent Neural Network Models for Blood Pressure Monitoring Using PPG Morphological Features." 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society. https://doi.org/10.1109/embc46164.2021.9630319.
Hajj, C. E. and Kyriacou, P. A. (2021) 'Recurrent Neural Network Models for Blood Pressure Monitoring Using PPG Morphological Features', 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society. doi: 10.1109/embc46164.2021.9630319.
Hajj, Chadi El, and Panayiotis A. Kyriacou "Recurrent Neural Network Models for Blood Pressure Monitoring Using PPG Morphological Features." 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021. https://doi.org/10.1109/embc46164.2021.9630319.
C. E. Hajj and P. A. Kyriacou, "Recurrent Neural Network Models for Blood Pressure Monitoring Using PPG Morphological Features," 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021, doi: 10.1109/embc46164.2021.9630319.
@inproceedings{hajj2021recurrent,
  title         = {{Recurrent Neural Network Models for Blood Pressure Monitoring Using PPG Morphological Features}},
  author        = {Hajj, Chadi El and Kyriacou, Panayiotis A.},
  year          = {2021},
  booktitle     = {2021 43rd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society},
  doi           = {10.1109/embc46164.2021.9630319}
}

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