Master of Science
Dr. Mark Daley
Dr. Lindi Wahl
Modern computational approaches tied together with the power of mathematical science has pushed us closer to reach a deeper understanding of complex dynamical systems. Real-world biological and physiological systems now can be studied on account of the accessibility to fast, cheap and powerful computers. In particular, the field of neuroscience and brain data analysis has grown significantly in the recent years. Recurrence plots (RPs) are a relatively new approach for the analysis of nonlinear, non-stationary and noisy data. Rooted in topological properties of the system, RP visualizes the recurrence states of the dynamical system. Armed with the recurrence quantification measures, RP is even more rigorous in exploring and quantifying real-world dynamical system.
In the present work, we benefit from the RP and RQA methods to study the behavior of intracranial pressure (ICP) waveforms. ICP is defined as the fluid pressure inside the skull which carries important information associated with the status of the patient. Our main goal is to detect sudden changes or extreme regime changing in these signals. Patterns appearing in RP can shed light on fundamental characteristics of the system. Our results suggest distinguishable patterns in the RPs of some subjects which are not detectable in the raw ICP signals. This work sets up the workflow for using RP analysis in online ICP monitoring of brain-injured patients.
Rabbani, Mahnaz, "The Recurrence-Based Analysis of Intracranial Pressure" (2017). Electronic Thesis and Dissertation Repository. 5156.