Affiliations: [a] College of Physics and Information Engineering, Fuzhou University, Fuzhou, China | [b] Fujian Centerm Information Co. Ltd, Fuzhou, China | [c] School of Computing and Mathematics, University of Ulster, Northern Ireland, UK
Correspondence:
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Address for correspondence: Huiru Zheng, School of Computing and Mathematics, University of Ulster at Jordanstown, Shore Road, Newtownabbey, Co. Antrim, BT37 0QB, Northern Ireland, UK. Tel.: +44 28 9036 6591; E-mail: [email protected].
Abstract: 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.
