Assessment of inflow and outflow stenoses usingbig 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 levelof DOS at the inflow and outflow sites. In contrast to the
multiple linear regression and traditional machine learning method, theexperimental results indicated that the
proposed screening model had higher accuracy (hit rate), true-positive rate, and true-negative rate in clinical indication
KeyWords
spectral data,fft algorithm,PAG,feature extraction
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