As inflow and outflow stenoses worsen, both flow resistance and pressure increase in the stenotic vascular access. During dialysis, when blood flow decreases, it may retrograde from the peripheral artery through the palmar arch to the arterial anastomosis site. Arterial steal syndrome (ASS) causes distal hypoperfusion, resulting in hand ischemia or extremity pain and edema. Hence, this study proposes the bilateral photoplethysmography (PPG) for ASS detection in arteriovenous fistulas. The decision-making quantizer utilizes the fractional-order feature extraction method and a non-cooperative game (NCG) framework to evaluate the ASS risk level. Bilateral asynchronous PPG signals have significant differences in the rise time and amplitude in relation to the degree of stenosis. The fractional-order self-synchronization error formulation is a feature extraction method used to quantify bilateral differences in blood flow changes between the dexter and sinister PPG signals. The NCG model as a method of decision-making is then employed to evaluate the ASS risk level. Using an acoustic Doppler measurement, the resistive (Res) index is also used to evaluate the vascular access stenosis at the arterial anastomosis site. In contrast with alternative methods including the high-sensitivity C-reactive protein level or Res index, our experimental results indicate that the proposed decision-making quantizer is more efficient in preventing ASS during hemodialysis treatment.
Atherosclerosis and resultant peripheral arterial disease (PAD) are common complications in patients with type 2 diabetes mellitus or end-stage renal disease and in elderly patients. The prevalence of PAD is higher in patients receiving haemodialysis therapy. For early assessment of arterial occlusion using bilateral photoplethysmography (PPG), such as changes in pulse transit time and pulse shape, bilateral timing differences could be used to identify the risk level of PAD. Hence, the authors propose a discrete fractional-order integrator to calculate the bilateral area under the systolic peak (AUSP). These indices indicated the differences in both rise-timing and amplitudes of PPG signals. The dexter and sinister AUSP ratios were preliminarily used to separate the normal condition from low/high risk of PAD. Then, transition probability-based decision-making model was employed to evaluate the risk levels. The joint probability could be specified as a critical threshold, < 0.81, to identify the true positive for screening low or high risk level of PAD, referring to the patients' health records. In contrast to the bilateral timing differences and traditional methods, the proposed model showed better efficiency in PAD assessments and provided a promising strategy to be implemented in an embedded system.
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