  The paper explores mathematical methods that differentiate regular and
chaotic time series, specifically for identifying pathological fistulas. It
proposes a noise-resistant method for classifying responding rows of normally
and pathologically functioning fistulas. This approach is grounded in the
hypothesis that laminar blood flow signifies normal function, while turbulent
flow indicates pathology. The study explores two distinct methods for
distinguishing chaotic from regular time series. The first method involves
mapping the time series onto the entropy-complexity plane and subsequently
comparing it to established clusters. The second method, introduced by the
authors, constructs a concepts-objects graph using formal concept analysis.
Both of these methods exhibit high efficiency in determining the state of the
fistula.

Medical Physics;Artificial Intelligence;Quantitative Methods