Abstract
This paper proposes the evaluation of arteriovenous shunt (AVS) stenosis using a fractional-order Fuzzy Petri net based screening system for long-term hemodialysis treatment of patients. The screening system uses the Burg method, the fractional-order chaos system, and the Fuzzy Petri net (FPN) for early detection of AVS dysfunction. The Burg method is an autoregressive (AR) model that is used to estimate the frequency spectra of a phonoangiographic signal and to identify the spectral peaks in the region from 25 Hz to 800 Hz. In AVS, the frequency spectrum varies between normal blood flow and turbulent flow. The power spectra demonstrate changes in frequency and amplitude as the degree of stenosis changes. A screening system combining fractional-order chaos system and FPN is used to track the differences in the frequency spectra between the normal and stenosis access. The dynamic errors are indexes that can be used to evaluate the degree of AVS stenosis using a FPN. For 42 long-term follow-up patients, testing results show that the proposed screening system is more efficient in the evaluation of AVS stenosis.
Highlights
How to cite this paper: Chen, W.-L., et al (2014) Arteriovenous Shunt Stenosis Evaluation Using a Fractional-Order Fuzzy Petri Net Based Screening System for Long-Term Hemodialysis Patients
The decision-making system developed in this study combines signal processing, fractional-order chaos system, and Fuzzy Petri net to evaluate the degree of arteriovenous shunt (AVS) stenosis
For 42 long-term follow-up patients, the results show that the proposed screening system is more efficient for AVS stenosis evaluation
Summary
How to cite this paper: Chen, W.-L., et al (2014) Arteriovenous Shunt Stenosis Evaluation Using a Fractional-Order Fuzzy Petri Net Based Screening System for Long-Term Hemodialysis Patients. Studies [10]-[12] have shown that stenosis produces a general increase in sound level and new high frequency components in the power spectra Frequency analysis, such as Fourier transform and wavelet transform, is used to preprocess the phonoangiographic signals. Chen-Lee based fractional-order chaos system is used to track the differences in the frequency spectra between normal condition and AVS stenosis. This approach uses a master system (MS) and a slave system (SS). The decision-making system developed in this study combines signal processing, fractional-order chaos system, and Fuzzy Petri net to evaluate the degree of AVS stenosis.
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