Classifying heartrate by change detection and wavelet methods for emergency physicians

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.