Aims: This study investigated the effects of using and combining multiscaling from the method multiscaled entropy analysis with Renyi entropy calculation on cardiac signals data. The hypothesis stated that the proposed multiscaled Renyi entropy would provide a robust method for the detection of cardiac signal complexity and the identification and differentiation between healthy and pathologic groups. The proposed method provided an insight into the effect of varying the entropy probability thresholding when computing Renyi entropy of RR interval signals at different scaling factors of time. Both methods showed enhanced results on their own, but they have not been studied before in a combined approach. Methods: 8-minute ECG recordings were collected from 90 participants from the open-access PhysioNet database and grouped into three types as normal sinus rhythm (healthy), cardiac arrhythmia (pathologic) and congestive heart failure (pathologic). A time coarse-graining algorithm based on a second moment method was used to obtain different temporal scales of the original signal. Then, Renyi entropy probabilities of each scale-factored signal were calculated using a method of density based on sequences of the RR interval time series rather than single values. T-test was used to determine significant differences between the multiscaled Renyi entropy measures of the different groups of RR interval signals. Results: The novel multiscaled Renyi entropy analysis provided a discrimination improvement by up to > 5% between healthy and pathological signals at p < 0.0005 and within the pathological signals at p < 0.003. Conclusion: Developing and applying a joint approach of a cardiac signal temporal multiscaling and calculating its Renyi entropy provides an optimized approach to identifying and separating healthy and pathological cardiac signals compared to using each of the methods individually.