wavelet coefficients are obtained, the energy for each coefficient are calculated as described in the literature. After calculating the normalized values of wavelet energies, which represent the relative wavelet energy (or the probability distribution), the wavelet entropies are obtained using the definition of entropy given by Shannon. Non-linear methods Given the complexity of the mechanisms regulating heart rate, it is reasonable to assume that applying HRV analysis based on methods of non-linear dynamics will yield valuable information. Although chaotic behavior has been assumed, more rigorous testing has shown that heart rate variability cannot be described as a low dimensional chaotic process. However, application of chaotic globals to HRV has been shown to predict diabetes status. The most commonly used non-linear method of analysing heart rate variability is the Poincaré plot. Each data point represents a pair of successive beats, the x-axis is the current RR interval, while the y-axis is the previous RR interval. HRV is quantified by fitting mathematically defined geometric shapes to the data. Other methods used are the correlation dimension, symbolic dynamics, nonlinear predictability, pointwise correlation dimension, approximate entropy, sample entropy, multiscale entropy analysis, sample asymmetry and memory length (based on inverse statistical analysis). It is also possible to represent long range correlations geometrically. Long term correlations Sequences of RR intervals have been found to have long-term correlations. However, one flaw with these analyses is their lack of goodness-of-fit statistics, i.e. values are derived that may or may not have adequate statistical rigor. Different types of correlations have been found during different sleep stages. Heart rate dependence of HRV parameters A basic problem is that all the parameters used to characterize HRV strongly depend on heart rate, but many articles have not adjusted properly or at all for HR differences when comparing HRV in multiple circumstances. However, the exact HRV(HR) relationship is still a matter of debate. For time domain parameters (RMSSD, SDNN, etc.) the results imply that, if there exists a universal function, it should be either exponential or hyperbolic in nature. The evaluation procedures used to determine HRV(HR) function have not allowed to decide between these options, so far. A new evaluation method has recently allowed to determine a HRV(HR) function with unprecedented precision: it can be described by two descending exponential components for healthy individuals, in general. Duration and circumstances of ECG recording Time domain methods are preferred to frequency domain methods when short-term