Issue |
ESAIM: ProcS
Volume 45, September 2014
Congrès SMAI 2013
|
|
---|---|---|
Page(s) | 48 - 57 | |
DOI | https://doi.org/10.1051/proc/201445005 | |
Published online | 13 November 2014 |
Classifying heartrate by change detection and wavelet methods for emergency physicians*
1 Laboratoire de Mathématiques, UMR
6620 CNRS et Université Blaise Pascal (Clermont-Ferrand 2),
France.
2 School of Exercise Science,
Australian Catholic University, Melbourne, Victoria, Australia
3 Department of Occupational Medicine,
University Hospital (CHU), G.
Montpied Hospital, Clermont-Ferrand, France
4 Laboratory of Metabolic Adaptations
to Exercise in Physiological and Pathological Conditions EA3533, Blaise Pascal
University, Clermont-Ferrand, France
5 Emergency Department, University
Hospital (CHU), G. Montpied
Hospital, Clermont-Ferrand, France
Heart Rate Variability (HRV) carries a wealth of information about the physiological state and the behaviour of a living individual. Indeed, the heart rate variation is intrinsically linked to the autonomic nervous system: the parasympathetic and orthosympathetic systems. Thus, any imbalance in these two opposite systems results in a variation of the cardiac frequency modulation. This alternation between equilibrium and disequilibrium (frequency variability) is recognized as an indicator of well-being and good health. Particularly, decreased HRV is linked to stress, fatigue and decreased physical performance. The aim of this work is to exploit the heart rate signals to detect stressful situations in different populations: emergency physicians, sportsmen, animal behaviours...We introduce a methodological framework for the detection of stress and eventually well-being. Our contribution is firstly based on using Gabor wavelets to extract energies corresponding to High and Low Frequency (HF and LF) bands which are linked to the parasympathetic and orthosympathetic systems. We then detect change points on these energies using the Filtered Derivative with p-value (FDpV) method. Finally, we develop a typology of cardiac activity by distinguishing homogeneous groups or state profiles sharing similar characteristics. We apply our methodology on a real dataset collected by monitoring cardiac activity of an emergency physician for 24 hours.
Résumé
La variabilité sinusale est porteuse de riches informations sur l’état physiologique et comportemental d’un individu. En effet, la variation du rythme cardiaque est intrinsèquement liée au système nerveux centrale autonome : les systèmes Parasympathique et Orthosympathique. Ainsi, tout déséquilibre dans ces deux systèmes se traduit par une variation de la modulation de la fréquence cardiaque. Cette alternance entre équilibre et déséquilibre (en l’occurrence ici une grande variabilité de la fréquence) est considérée comme un indicateur de bonne santé : une diminution de la variabilité sinusale est liée au stress, à la fatigue et à la diminution des performances physiques. Le but de ce travail est d’exploiter le rythme cardiaque pour détecter des situations de stress dans différentes populations : médecins urgentistes, sportifs amateurs, comportements animaliers... Nous élaborons une typologie de l’activité cardiaque en distinguant des groupes homogènes ou des profils d’états partageant une certaine ressemblance.
Nous appliquons ensuite notre méthodologie à un jeu de données réelles correspondant à une garde d’un médecin urgentiste.
© EDP Sciences, SMAI 2014
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