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.

提出了一种噪声抵抗方法，通过数学方法区分正常和混沌时间序列，特别是在识别病理性瘘管方面；该方法基于以下假设：层流血流表示正常功能，而湍流血流表示病理性。研究探索了两种区别混沌和规则时间序列的方法，第一种方法是将时间序列映射到复杂熵平面，并将其与已建立的聚类进行比较；第二种方法是通过使用形式概念分析构建概念-对象图。这两种方法都表现出高效地确定瘘管状态的能力。

基于日期的血透血管瘤识别方法：熵复杂性和形式概念分析