Classifying heartrate by change detection and wavelet methods for emergency physicians

Abstract

Abstract. Heart Rate Variability (HRV) carries a wealth of information about the physiological state and the behaviour of a living subject. Indeed, the heart rate variation is intrinsically linked to the autonomic nervous system: the Parasympathetic and Sympathetic systems. Thus, any imbalance in these two opposite systems results in a variation of the cardiac frequency modulation. It is also recognized that this alternation between equilibrium and disequilibrium (frequency variability) is an indicator of well being and good health. In other words, decreased heart rate variability is always linked to stress, fatigue and decreased physical performances. The aim of this work is to exploit the heart rate signals to detect situations of stress in different populations: emergency physicians, sportsmen, animal behaviours, etc. . . This paper introduces a methodological framework for the detection of stress and eventually well being. Our contribution is based on first extracting high and low frequencies energies which are linked to the Parasympathetic and Sympathetic 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 having a characteristic similarity.We apply our methodology on a real dataset corresponding to an emergency doctor.Résumé. La variabilité sinusale est porteuse de riches informations sur l'état physiologique et comportemental d'un sujet vivant. En effet, la variation du rythme cardiaque est intrinsèquement liée au système nerveux autonome: les systèmes parasympathique et orthosympathique. Ainsi, tout déséquilibre dans ces deux systèmes se traduit par une variation de la modulation en fréquence cardiaque. Il estégalement admis que cette alternance entreéquilibre et déséquilibre (en l'occurrence ici une grande variabilité de la fréquence) est un indicateur de bonne santé : une diminution de la variabilité sinusale est toujours liée au stress,à la fatigue et au diminution des performances physiques. Le but de ce travail est d'exploiter le rythme cardiaque pour détecter des situations de stress dans différentes populations: medecins urgentistes, sportifs amateurs, comportement d'animaux... Nouś elaborons une typologie de l'activité cardiaque en distinguant des groupes homogènes ou des profils d'états ayant une certaine ressemblance. Nous appliquons ensuite notre méthodologieà un jeu de données réelles correspondantà une garde d'un médecin urgentiste.

‘Do Well B.’: Design Of WELL Being monitoring systems. A study protocol for the application in autism

“… 35 Eventually, after having performed a Principal Component Analysis, 39 we will disentangle clusters of variables characterising different behaviours of RR-intervals series such as anxiety, rest and physical activity. 40 …”

The Need for Objective Measures of Stress in Autism

“…Due to the important amount of data to be processed, HRV require offline analysis witch is not compatible with real-time evaluation of stress. New method in development, that use detection of abrupt changes in HRV, will allow the identification of stressful events (Azzaoui et al, 2014 ; Dutheil et al, 2015 ). Heart rate, and thus HRV, are one of the easiest physiological measurements accessible to the general public.…”