Geodesic
Classifying heartrate by change detection and wavelet methods for emergency physicians
Nourddine Azzaoui1 ; Arnaud Guillin1 ; Frederic Dutheil2, 3, 4, 5 ; Gil Boudet3 ; Alain Chamoux4 ; Christophe Perrier5 ; Jeannot Schmidt5 ; Pierre Raphaël Bertrand1
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
ESAIM. Proceedings, Tome 45 (2014), pp. 48-57.

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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.
DOI : 10.1051/proc/201445005

Nourddine Azzaoui 1 ; Arnaud Guillin 1 ; Frederic Dutheil 2, 3, 4, 5 ; Gil Boudet 3 ; Alain Chamoux 4 ; Christophe Perrier 5 ; Jeannot Schmidt 5 ; Pierre Raphaël Bertrand 1

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 
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     author = {Nourddine Azzaoui and Arnaud Guillin and Frederic Dutheil and Gil Boudet and Alain Chamoux and Christophe Perrier and Jeannot Schmidt and Pierre Rapha\"el Bertrand},
     title = {Classifying heartrate by change detection and wavelet methods for emergency physicians},
     journal = {ESAIM. Proceedings},
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Nourddine Azzaoui; Arnaud Guillin; Frederic Dutheil; Gil Boudet; Alain Chamoux; Christophe Perrier; Jeannot Schmidt; Pierre Raphaël Bertrand. Classifying heartrate by change detection and wavelet methods for emergency physicians. ESAIM. Proceedings, Tome 45 (2014), pp. 48-57. doi : 10.1051/proc/201445005. http://geodesic.mathdoc.fr/articles/10.1051/proc/201445005/

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