Why the study?
Does a flexible fuzzy classifier based on auscultation accurately estimate residual stenosis in arteriovenous shunts in hemodialysis patients?
Does a flexible fuzzy classifier based on auscultation accurately estimate residual stenosis in arteriovenous shunts in hemodialysis patients?
A novel noninvasive auscultation-based flexible fuzzy classifier can efficiently estimate residual stenosis in arteriovenous shunts for hemodialysis patients.
May aid noninvasive AV shunt monitoring; leaves open prospective validation in larger cohorts before clinical use.
This article determines whether the diameter of a vascular lumen is clinically valuable for hemodialysis therapy. A novel method is proposed that uses a flexible fuzzy classifier (FFC) implementing a curve approximation function to estimate residual stenosis in an arteriovenous shunt (AVS). The auscultation method provides a noninvasive technique for monitoring the degree of AVS stenoses. The Burg autoregressive method is used to identify the characteristic spectra for changes in frequency and amplitude between frequencies of 25 and 800 Hz. Fractional order self-synchronization error formulation is then used to quantify the degree of stenosis between normal access and AVS stenosis in the frequency spectra. To estimate the diameter of a vascular lumen, the FFC is used to create a nonlinear prediction model. For the 42 long-term patients studied, the results of this examination show that the proposed prediction model estimates residual stenosis efficiently.
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Du et al. (2014) studied this question.
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