Learning to predict coronary perfusion pressure during cardiopulmonary resuscitation

Manan Gandhi, Yunpeng Pan, Evangelos Theodorou, Pierre Sebastian, Matt Olson, Demetri Yannopoulos

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


The goal of this work is to advance the capability of automated, mechanical cardiopulmonary resuscitation (CPR) by predicting Coronary Perfusion Pressure (CPP) within 5 mmHg at a given moment in time. We aim to utilize methods from machine learning in order to model the CPP of a porcine patient subjected to automated chest compressions. During preprocessing of the data, we show how data sampling rate, delays and moving average filtering can improve predictions. We demonstrate state of the art modeling performance utilizing a variety of algorithms, and analyze the performance of each algorithm for single-step and long-term predictions. The results indicate that a delayed linear system achieves this target CPP within 0.25 mmHg. For longer time horizons, a more complex model is required. We demonstrate that the Long-short-term-memory (LSTM) network has the best single run performance, while the Sparse Spectrum Gaussian Process (SSGP) has the best average performance.

Original languageEnglish (US)
Title of host publicationAdvances in Control Design Methods; Advances in Nonlinear Control; Advances in Robotics; Assistive and Rehabilitation Robotics; Automotive Dynamics and Emerging Powertrain Technologies; Automotive Systems; Bio Engineering Applications; Bio-Mechatronics and Physical Human Robot Interaction; Biomedical and Neural Systems; Biomedical and Neural Systems Modeling, Diagnostics, and Healthcare
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791851890
StatePublished - 2018
EventASME 2018 Dynamic Systems and Control Conference, DSCC 2018 - Atlanta, United States
Duration: Sep 30 2018Oct 3 2018

Publication series

NameASME 2018 Dynamic Systems and Control Conference, DSCC 2018


OtherASME 2018 Dynamic Systems and Control Conference, DSCC 2018
Country/TerritoryUnited States

Bibliographical note

Funding Information:
This work was supported by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program.

Publisher Copyright:
Copyright © 2018 ASME.


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