Detection of venous needle dislodgement during haemodialysis using fractional order shape index ratio and fuzzy colour relation analysis. Issue 6 (19th November 2015)

• Record Type: Journal Article
• Authors: Lin, Chia‐Hung; Chen, Wei‐Ling; Kan, Chung‐Dann; Wu, Ming‐Jui; Mai, Yi‐Chen
• Is Part Of: Healthcare technology letters. Volume 2:Issue 6(2015)
• Page Start: 149
• Page End: 155
• Publication Date: 2015-11-19
• Subjects: cardiology -- regression analysis -- risk analysis -- blood pressure measurement -- patient treatment -- patient monitoring; risk level -- TVP variation -- linear regression -- transverse vibration pressure -- venous anatomic site -- heart pressure wave variation -- heart pulse variation -- haemodialysis care -- blood loss -- haemodialysis treatment -- colour relation analysis -- fuzzy CRA -- shape index ratio -- fractional order SIR -- VND detection -- venous needle dislodgement; Biomedical engineering -- Periodicals; Medical technology -- Periodicals; 610.28
• DOI: 10.1049/htl.2015.0022
• Languages: English
• ISSNs: 2053-3713

Abstract

Abstract : Venous needle dislodgement (VND) is a life‐threatening complication during haemodialysis (HD) treatment. When VND occurs, it only takes a few minutes for blood loss in an adult patient. According to the ANNA (American Nephrology Nurses' Association) VND survey reports, VND is a concerning issue for the nephrology nurses/staff and patients. To ensure HD care and an effective treatment environment, this Letter proposes a combination of fractional order shape index ratio (SIR) and fuzzy colour relation analysis (CRA) to detect VND. If the venous needle drops out, clinical examinations show that both heart pulses and pressure wave variations have a low correlation at the venous anatomic site. Therefore, fractional order SIR is used to quantify the differences in transverse vibration pressures (TVPs) between the normal condition and meter reading. Linear regression shows that the fractional order SIR has a high correlation with the TVP variation. Fuzzy CRA is designed in a simple and visual message manner to identify the risk levels. A worst‐case study demonstrated that the proposed model can be used for VND detection in clinical applications.