1D Convolutional Neural Networks for Estimation of Compensatory Reserve from Blood Pressure Waveforms

Robert W. Techentin, Christopher L. Felton, Taylor E. Schlotman, Barry K. Gilbert, Michael J. Joyner, Timothy B. Curry, Victor A. Convertino, David R. Holmes, Clifton R. Haider

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a Deep Convolutional Neural Network (CNN) architecture for computing a Compensatory Reserve Metric (CRM) for trauma victims suffering from hypovolemia (decreased circulating blood volume). The CRM is a single health indicator value that ranges from 100% for healthy individuals, down to 0% at hemodynamic decompensation -when the body can no longer compensate for blood loss. The CNN is trained on 20 second blood pressure waveform segments obtained from a finger-cuff monitor of 194 subjects. The model accurately predicts CRM when tested on data from 22 additional human subjects obtained from Lower Body Negative Pressure (LBNP) emulation of hemorrhage, attaining a mean squared error (MSE) of 0.0238 over the full range of values, including those from subjects with both low and high tolerance to central hypovolemia.

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ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

Techentin, R. W., Felton, C. L., Schlotman, T. E., Gilbert, B. K., Joyner, M. J., Curry, T. B., Convertino, V. A., Holmes, D. R., & Haider, C. R. (2019). 1D Convolutional Neural Networks for Estimation of Compensatory Reserve from Blood Pressure Waveforms. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 (pp. 2169-2173). [8857116] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2019.8857116