Heart rate variability (HRV) or the study of cardiac rhythm fluctuations has attracted the interest of many physicists in the recent years for its potential predictive value. The extracted and analyzed heart rate variability signal parameters, are highly useful in diagnostics. The HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity and may contain indicators of current behavior, or even warnings about impending diseases. This work deals with the study of the ECG (electrocardiogram) and the application of a nonlinear dynamic method in the analysis of the HRV (heart rate variability) towards clinical application. Tiresome diagnosis. The analysis method adopted for this purpose. the DFA (detrended fluctuation analysis) which is a nonlinear dynamic method based on the quantification of fractal like correlation properties of the data, shows through the obtained results, its efficiency in the prediction field of the current and the future health behavior. The study is made by analyzing a group of 10 healthy cases practicing several activities. For each one 24 hours ECG (RR intervals) was recorded. Statistical analysis was performed by means of non parametric Mann-Whitney test. The analysis show plots shape depends on the current behavior and the toughness of the activity. Our study reveals that by using DFA method, we could quantitatively and qualitatively study the heart rate variability towards tire some diagnosis.
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