Affiliation:
1. University of Kelaniya
2. Asian Institute of Technology
Abstract
Continuous measurement of the Blood Pressure (BP) is important in hypertensive patientsand elderly population. Traditional cuff based methods are difficult to use since it is uncomfortable towear a cuff throughout the day. A more suitable method is to estimate the BP using the Photoplethysmography(PPG) signal. However, it is difficult to estimate a BP when the PPG is corrupted withMotion Artifacts (MAs). In this paper, Long Short Term Memory (LSTM) an extension of RecurrentNeural Networks (RNN) is used used to improve the accuracy of the estimation of the BP from thecorrupted PPG. It shows that an accuracy of 97.86 is achieved.
Publisher
Trans Tech Publications, Ltd.
Reference11 articles.
1. X. Xing and M. Sun, Optical Blood Pressure Estimation with Photoplethysmography and FFTbased Neural Networks, Biomedical Optics Express, 7, 3007-3020 (2016).
2. Z. Zhang, Photoplethysmography-Based Heart Rate Monitoring in Physical Activities via Joint Sparse Spectrum Reconstruction, IEEE Transactions in Biomedical Engineering, 62,1902-1910 (2015).
3. B. M. Jayadevappa and S. H. Mallikarjun, An Estimation Technique using FFT for Heart Rate Derived from PPG Signal, Global Journal of Researches in Engineering: F Electrical and Electronics Engineering, 15, 45-51 (2015).
4. P. Wei, R. Guo, J. Zhang, and Y. T. Zhang, A New Wristband Wearable Sensor Using Adaptive Reduction Filter to Reduce Motion Artifact, 5th International Conference on Information Technology and Application in Biomedicine (2008).
5. A. Visvanathan, A. Sinha, and A. Pal, Estimation of blood pressure levels from reflective Photoplethysmograph using smart phones, 13th IEEE International Conference on BioInformatics and BioEngineering, 1-5 (2013).