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===== Title : Model based analysis of the Autonomous Nervous System influence on the heart intervals =====
 
Intervenant : [[http://www.i3s.unice.fr/~meste/|Olivier Meste]], Professeur à l'Université de Nice Sophia Antipolis, responsable de l'équipe BIOMED du Laboratoire Informatique Signaux et Systèmes de Sophia, I3S UMR CNRS 7271

Respiratory Sinus Arrhythmia (RSA) is a well-known cardio-respiratory coupling that results in the modulation of sinus rhythm by breathing. At rest, RSA improves the efficiency of pulmonary gas exchange in matching pulmonary blood flow to lung inflation and buffers blood pressure and cardiac output variations mechanically induced by respiration. This phenomenon is a good example of the influence of the Autonomous Nervous System (ANS) over the heart rhythm in order to adjust the blood flow to the body demand. Body surface Electrocardiogram provides an amount of information that allows an indirect assessment of such influence through the analysis of cardiac intervals.

The heart period or heart rate is a well-known intervals series modeled and studied in numerous scientific papers to this aim. Despite such extensive research, the classical Integral Pulse Frequency Modulation (IPFM) has been improved and extended to the dynamical case of the stress test exercise where respiration profile is crucial. As previously mentioned, respiratory system play an important role in the heart rate variability (HRV). The magnitude of the respiration component observed in the HRV signal is varying as long as the ANS regulates the physiological variables, making possible its indirect activation assessment. It has been recently shown that in contrast to well-accepted assumption that respiration is the highest frequency component in the HR, higher components exist when performing cycling.    

In spite of a variable frequency, the filtering of the HRV signal is still feasible provided that this time-varying frequency is known. The Short-Time Fourier Transform is a convenient tool for such unbiased time-varying filtering when local properties of the signal are assumed. When the frequency is unknown time-varying autoregressive model based on linear combination of basis functions can be introduced in order to track the frequency variations.

Whereas the HRV analysis is well documented it will be shown that the proposed approaches and models bring new knowledge on the mechanical and neural modulation of the heart rate. To support this statement it will be depicted the time-varying pattern of HRV spectrum through different pedaling frequencies and strengths, during mild and maximal graded cycling test performed by sedentary, athletes and heart transplanted subjects. In addition, the P-R interval will be presented as an alternative to the HRV analysis for the ANS activity assessment. In contrast to HRV based approach, the P-R intervals analysis clearly exhibits the pattern of the abrupt parasympathetic return in the early recovery phase of the exercise test.

One objective of this talk is to convince attendees that simple ECG conveys various information on the physical status of the subject. Accurate remote monitoring for patient follow-up or for population at risk is a possible application.   


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