Paperdoi:10.1109/embc44109.2020.9175699
Cuffless and Continuous Blood Pressure Estimation From PPG Signals Using Recurrent Neural Networks
2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
TL;DR
This paper proposes cuffless and continuous blood pressure estimation utilising Photoplethysmography (PPG) signals and state of the art recurrent network models, namely, Long Short Term Memory and Gated Recurrent Units. The models were validated on wide range of varying blood pressure and PPG signals acquired from the Multiparameter Intelligent Monitoring in Intensive Care database. Many features were extracted from the PPG waveform and several machine learning techniques were employed in an attempt to eliminate collinearity and reduce the size of input feature vector.
- Published in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (2020).
Abstract
This paper proposes cuffless and continuous blood pressure estimation utilising Photoplethysmography (PPG) signals and state of the art recurrent network models, namely, Long Short Term Memory and Gated Recurrent Units. The models were validated on wide range of varying blood pressure and PPG signals acquired from the Multiparameter Intelligent Monitoring in Intensive Care database. Many features were extracted from the PPG waveform and several machine learning techniques were employed in an attempt to eliminate collinearity and reduce the size of input feature vector. Consequently, the most effective features for blood pressure estimation were selected. Experimental results show that the accuracy of the proposed methods outperform traditional models applied in the literature. The results satisfy the American National Standards of the Association for the Advancement of Medical Instrumentation.
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Hajj, C. E., & Kyriacou, P. A. (2020). Cuffless and Continuous Blood Pressure Estimation From PPG Signals Using Recurrent Neural Networks. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society. https://doi.org/10.1109/embc44109.2020.9175699
Hajj, Chadi El and Panayiotis A. Kyriacou. 2020. "Cuffless and Continuous Blood Pressure Estimation From PPG Signals Using Recurrent Neural Networks." 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society. https://doi.org/10.1109/embc44109.2020.9175699.
Hajj, C. E. and Kyriacou, P. A. (2020) 'Cuffless and Continuous Blood Pressure Estimation From PPG Signals Using Recurrent Neural Networks', 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society. doi: 10.1109/embc44109.2020.9175699.
Hajj, Chadi El, and Panayiotis A. Kyriacou "Cuffless and Continuous Blood Pressure Estimation From PPG Signals Using Recurrent Neural Networks." 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020. https://doi.org/10.1109/embc44109.2020.9175699.
C. E. Hajj and P. A. Kyriacou, "Cuffless and Continuous Blood Pressure Estimation From PPG Signals Using Recurrent Neural Networks," 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2020, doi: 10.1109/embc44109.2020.9175699.
@inproceedings{hajj2020cuffless,
title = {{Cuffless and Continuous Blood Pressure Estimation From PPG Signals Using Recurrent Neural Networks}},
author = {Hajj, Chadi El and Kyriacou, Panayiotis A.},
year = {2020},
booktitle = {2020 42nd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society},
doi = {10.1109/embc44109.2020.9175699}
}Added to the ChatPPG research hub
