Bilateral photoplethysmography for arterial steal detection in arteriovenous fistula using a fractional-order decision-making quantizer

Jian Xing Wu, Guan Chun Chen, Ming Jui Wu, Chia Hung Lin, Tainsong Chen

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)257-270
Number of pages14
JournalMedical and Biological Engineering and Computing
Volume55
Issue number2
DOIs
Publication statusPublished - 2017 Feb 1

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Computer Science Applications

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