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Automatic detection of exercise oscillatory ventilation in cardiopulmonary exercise testing: developing an accurate and practical graphical user interface.

Session Poster Session II - Thursday 14:00 - 18:00

Speaker Justien Cornelis

Event : ESC Preventive Cardiology (Formerly EuroPrevent) 2015

  • Topic : preventive cardiology
  • Sub-topic : Exercise Testing
  • Session type : Poster Session

Authors : J Cornelis (Antwerp,BE), K Poppe (Antwerp,BE), Q Claes (Antwerp,BE), M Deconinck (Antwerp,BE), T Van Assche (Antwerp,BE), P Beckers (Antwerp,BE), C Vrints (Antwerp,BE), D Vissers (Antwerp,BE), M Goossens (Antwerp,BE)

J Cornelis1 , K Poppe2 , Q Claes2 , M Deconinck2 , T Van Assche2 , P Beckers3 , C Vrints3 , D Vissers1 , M Goossens2 , 1University of Antwerp, Department of Physiotherapy (REVAKI) - Antwerp - Belgium , 2University of Antwerp, Applied Engineering - Antwerp - Belgium , 3University of Antwerp Hospital (Edegem), Department of Cardiology - Antwerp - Belgium ,


Purpose: Exercise Oscillatory Ventilation (EOV) is a prognostic marker assessed during a Cardio Pulmonary Exercise Test (CPET) predicting early mortality in patients with Chronic Heart Failure (CHF). Up till now a gold standard definition is not described. This case study implements four of the commonly used definitions of EOV into a Graphical User Interface (GUI) in order to automate, compare and objectively assess EOV in an accurate, practical and consistent way.

Methods: Breath-by-breath Minute Ventilation, collected during incremental CPET bicycle Ramp test (40Watt+20Watt/min) of a CHF patient with possible EOV, was analysed retrospectively. Several wavelet transformations were applied and compared, resulting in the use of a 2nd level discrete Meyer transformation. In order to quantify the oscillations, a local minima, -maxima and related time interval detection algorithm was implemented. This way the length between two local minima and the height of each oscillation could be determined. The number of oscillations according to the proposed criteria was recorded (marked zones in figure) and expressed as a percentage of the total exercise time.

Results: This novel GUI provides a visual presentation (see figure) of areas meeting the criteria according to the respective implemented definitions. In this case, EOV was present according all four formulae.

Conclusion: Automatic detection of EOV using a pre-programmed GUI could be a more valid and reliable method compared to visual or manual detection. It gives the possibility to overview the results of existing formulae at a glance. Moreover, this GUI could be integrated in existing CPET software making computerized analysis of EOV readily available.

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