FEEDFORWARD NEURAL NETWORK APPROACH FOR BLOOD PRESSURE ESTIMATION FROM PPG SIGNALS
DOI:
https://doi.org/10.24867/29BE17JorgovanovicKeywords:
Blood pressure, artificial, artificial neural networks, feedforward neural network, photoplethysmographyAbstract
This article addresses the procedure for estimating blood pressure, based on features extracted from PPG signals, using artificial neural networks. Multiple models have been made so as to find the best combination of characteristics, of the PPG signal, that correlate to blood pressure the most. The results from all models are then directly compared.
References
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[3] A. Poliński, J. Kot, A. Meresta, "Analysis of correlation between heart rate and blood pressure" Federated Conference on Computer Science and Information Systems, 2011.
[4] Y. Liang, Z. Chen, G. Liu, M. Elgendi, "A new, short-recorded photoplethysmogram dataset for blood pressure monitoring in China," Scientific Data (Sci Data), 2018.
[5] J. H.-S. Wang, M.-H. Yeh, P. C.-P. Chao, T.-Y. Tu, Y.-H. Kao, R. Pandey, "A fast digital chip implementing a real-time noise-resistant algorithm for estimating blood pressure using a non-invasive, cuffless PPG sensor" Microsystem Technologies, 2020.
[6] E. O'Brien, B. Waeber, G. Parati, J. Staessen, M.G. Myers, "Blood pressure measuring devices: Recommendations of the European Society of Hypertension." BMJ, 2001.
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Published
2024-11-01
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Section
Electrotechnical and Computer Engineering
