Date-Driven Approach for Identifying State of Hemodialysis Fistulas: Entropy-Complexity and Formal Concept Analysis

TL;DR

This paper presents two novel mathematical approaches, entropy-complexity analysis and formal concept analysis, to accurately classify the health status of hemodialysis fistulas based on blood flow time series, aiding early diagnosis.

Contribution

It introduces a noise-resistant classification method combining entropy-complexity mapping and formal concept analysis for fistula health assessment.

Findings

High accuracy in distinguishing normal and pathological fistulas

Effective noise resistance in classification methods

Potential for early diagnosis of fistula complications

Abstract

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.