Electrical signals produced in the human body (ECG, EEG, CTG) are now part of standard medical diagnostics. However, the signature, of the dynamics that can be derived from physics, in these signals has a greater capacity than the one currently used in standard diagnostics. The idea of this thesis is to explore the potential of variables derived from the analysis of a time series of said physiological signals. This paper uses a detrended fluctuation analysis that eliminates the trend for determining correlations in each signal and multipath entropy of the cause by which information on the complexity of time series is obtained. Analyzing the fluctuations shows that long-range correlations exist in heart rate of a healthy people. In congestive heart failure, we see a crossover phenomenon that indicates different dynamics for small and large scales. In addition, a fluctuation analysis is applied to therapy evaluation. Multiscale sample entropy method shows that we have greater entropy values of this measure for the healthy heart’s dynamic. The complexity of these signals is greater than the complexity of those corresponding to certain pathological conditions. In addition, in the analysis of heart rate dynamics, it is shown that the value of sample entropy depends on the length of the time series - for shorter time series we have bigger entropy value, and for longer time series value decreases. Data analysis that corresponds to asymptomatic ventricular arrhythmia and congestive heart failure has shown that the associated time series are less complex and certain drugs used to treat arrhythmia reduce the complexity of time series.