Physiologic systems generate complex fluctuations in their output signals that reflect the underlying dynamics. Therefore, finding and analyzing hidden dynamical structures of these signals are of both basic and clinical interest. One approach to discovering such hidden information is to analyze the repetitive appearance of certain basic patterns that are embedded in the complete signals. We developed a novel information-based similarity index to detect and quantify such temporal structures in the human heart rate time series using tools from statistical linguistics [1].