Vascular access stenosis and venous needle dislodgement (VND) are frequent and serious life-threatening complications during hemodialysis (HD). According to dialysis survey reports, these complications are key issues for nephrology nurses, medical staff, and patients. Existing detection techniques and early warning tools provide promising solutions in these issues. However, these methods cannot screen for stenosis and VND complications during HD. Clinical examinations show that increases in transverse vibration pressure (TVP) are highly correlated with stenosis screening in arterial anastomosis sites (inflow needle); conversely, TVP drops in the event of a blood leak or VND in venous anastomosis sites (outflow needle). As an early-warning implementation, this study proposes a combination of fractional order integrator (FOI) and info-gap (IG) decision-making to detect these complications. FOI is used to calculate the TVPs' area under curve (AUC), while AUC ratio (AUCR) quantifies the differences in TVPs between the normal condition and pressure sensor reading. An estimated function of the two-point form shows that AUCRs have a high correlation with TVP variations. Therefore, AUCR is employed to identify changes in TVPs and arrange specific allocations. The IG decision-making scheme produces inference profiles with specific allocations to separate the normal cases from vascular access stenosis or VND/blood leakage. The test results obtained from practical experiments were validated and compared with the results obtained using existing methods such as acoustic techniques, warning products, and homodynamic analysis. The findings also show that the proposed framework employing pressure sensors can be implemented in an early warning monitor for clinical and telemedicine applications.
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Artificial Intelligence