Self-Similarity in the Heartbeat Time Series of Healthy Cardiac Systems in Mice

This study examined whether fractal dynamics in physiology may explain hidden information in physiologic time series and provide new approaches to monitor cardiac disease. It was hypothesized that self-similarity, which was displayed in fractal geometry, was observed in the cardiac interbeat series. Our method used physiologic data and readings from a dynamical self-test to illustrate the output of healthy cardiac systems. These illustrations of healthy systems were compared with fractal geometry. The applications of fractals were analyzed using open-source data and algorithms such as the Detrended Fluctuation Analysis (DFA) Algorithm. It was concluded that self-similarity was displayed in the healthy heart rate, and the DFA analysis was able to detect irregularity in a disease model. This study potentially could be used to enhance understanding and predicting possible applications of fractal dynamics to a variety of biomedical problems.