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

doi:10.1109/EMBC.2019.8857116

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 proceeding › Conference 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.

Original language	English (US)
Title of host publication	2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2169-2173
Number of pages	5
ISBN (Electronic)	9781538613115
DOIs	https://doi.org/10.1109/EMBC.2019.8857116
State	Published - Jul 2019
Event	41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 - Berlin, Germany Duration: Jul 23 2019 → Jul 27 2019

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Conference

Conference	41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Country/Territory	Germany
City	Berlin
Period	7/23/19 → 7/27/19

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC.2019.8857116

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). Article 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

1D Convolutional Neural Networks for Estimation of Compensatory Reserve from Blood Pressure Waveforms. / Techentin, Robert W.; Felton, Christopher L.; Schlotman, Taylor E. et al.
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2169-2173 8857116 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Techentin, RW, Felton, CL, Schlotman, TE, Gilbert, BK , Joyner, MJ , Curry, TB, Convertino, VA, Holmes, DR & Haider, CR 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., 8857116, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., pp. 2169-2173, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019, Berlin, Germany, 7/23/19. https://doi.org/10.1109/EMBC.2019.8857116

Techentin RW, Felton CL, Schlotman TE, Gilbert BK , Joyner MJ , Curry TB et al. 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. Institute of Electrical and Electronics Engineers Inc. 2019. p. 2169-2173. 8857116. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2019.8857116

Techentin, Robert W. ; Felton, Christopher L. ; Schlotman, Taylor E. et al. / 1D Convolutional Neural Networks for Estimation of Compensatory Reserve from Blood Pressure Waveforms. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 2169-2173 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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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.",

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

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AU - Joyner, Michael J.

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AU - Holmes, David R.

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

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