access icon openaccess Assessment of inflow and outflow stenoses using big spectral data and radial-based colour relation analysis on in vitro arteriovenous graft biophysical experimental model

Dialysis vascular accesses are critical for dialysis therapy, but they frequently suffer from stenotic complications. Higher patency rates and thrombosis rates are a concern to nephrology nurses and patients. These complications are complex events, including inflow stenosis, outflow stenosis, and coexistence of both. Therefore, a biophysical experimental model is employed to mimic the various combinations of stenoses and dialysis circulation circuits on a virtual adult hand. Considering the suggested signal preprocessing specifications, auscultation method and frequency analysis technique are used to extract the key frequency components from sufficient big spectral data. Key frequency components, depending on the degree of stenosis (DOS) (positive correlation), are validated using multiple regression models with multiple explanatory variables and response variables. A new machine learning method, radial-based colour relation analysis, is employed to identify the level of DOS at the inflow and outflow sites. In contrast to the multiple linear regression and traditional machine learning method, the experimental results indicated that the proposed screening model had higher accuracy (hit rate), true-positive rate, and true-negative rate in clinical indication.

Inspec keywords: patient treatment; Big Data; diseases; learning (artificial intelligence); medical signal processing

Other keywords: frequency analysis technique; degree of stenosis; auscultation method; outflow stenoses; patency rates; virtual adult hand; biophysical experimental model; in vitro arteriovenous graft biophysical experimental model; stenotic complications; thrombosis rates; dialysis vascular accesses; signal preprocessing specifications; machine learning method; nephrology nurses; DOS; inflow stenoses; dialysis circulation circuits; dialysis therapy; big spectral data; clinical indication; radial-based colour relation analysis

Subjects: Biomedical measurement and imaging; Knowledge engineering techniques; Data handling techniques; Digital signal processing; Biology and medical computing; Signal processing and detection; Patient diagnostic methods and instrumentation

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