Dynamic electrocardiogram (ECG) plays an important role in the analysis of heart movement and cardio-diseases. In an attempt to obtain a better understanding of the mechanisms of patterns and differences found in dynamic ECGs, techniques based on different theories such as chaos and fractal theory have been used to extract nonlinear information encoding in dynamics ECG signals. In this paper, we propose an information theory approach to supporting the analysis of dynamic ECG recorded during different time of a day. Mutual information of R-R intervals extracted from four subject groups were calculated and analysed. Results indicate that heart movement is similar to chaotic movement in many ways. Moreover, the mutual information of R-R intervals exhibits different patterns over different periods of a day and different subject groups, suggesting that it would be a useful tool to support classification analysis of heart movement and cardio related diseases.