Predicting cardiopulmonary response to incremental exercise test

Elena Baralis, Tania Cerquitelli, Silvia Chiusano, Andrea Giordano, Alessandro Mezzani, Davide Susta, Xin Xiao

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

Abstract

Cardiopulmonary exercise testing is a non-invasive method widely used to monitor various physiological signals, describing the cardiac and respiratory response of the patient to increasing workload. Since this method is physically very demanding, innovative data analysis techniques are needed to predict patient response thus lowering body stress and avoiding cardiopulmonary overload. This paper proposes the Cardiopulmonary Response Prediction (CRP) framework for early predicting the physiological signal values that can be reached during an incremental exercise test. The learning phase creates different models tailored to specific conditions (i.e., single-test and multiple-test models). Each model can be exploited in the real-time stream prediction phase to periodically predict, during the test execution, signal values achievable by the patient. Experimental results on a real dataset showed that CRP prediction is performed with a limited and acceptable error.

Original languageEnglish
Title of host publicationProceedings - IEEE Symposium on Computer-Based Medical Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages135-140
Number of pages6
Volume2015-July
ISBN (Print)9781467367752
DOIs
Publication statusPublished - Jul 24 2015
Event28th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2015 - Sao Carlos and Ribeirao Preto, Brazil
Duration: Jun 22 2015Jun 25 2015

Other

Other28th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2015
CountryBrazil
CitySao Carlos and Ribeirao Preto
Period6/22/156/25/15

Keywords

  • artificial neural networks
  • incremental test
  • physiological signals analysis
  • support vector machines

ASJC Scopus subject areas

  • Computer Science Applications
  • Radiology Nuclear Medicine and imaging

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