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

• Main Authors: Wei-Ling Chen, Chung-Dann Kan, Chia-Hung Lin
• Format: Article
• Language: English
• Published: Wiley 2017-02-01
• Series: IET Cyber-Physical Systems
• Subjects: Big Data; patient treatment; medical signal processing; learning (artificial intelligence); diseases; clinical indication; machine learning method; DOS; degree of stenosis; frequency analysis technique; auscultation method; signal preprocessing specifications; virtual adult hand; dialysis circulation circuits; biophysical experimental model; nephrology nurses; thrombosis rates; patency rates; stenotic complications; dialysis therapy; dialysis vascular accesses; in vitro arteriovenous graft biophysical experimental model; radial-based colour relation analysis; big spectral data; outflow stenoses; inflow stenoses
• Online Access: https://digital-library.theiet.org/content/journals/10.1049/iet-cps.2016.0040

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.