Phonographic signal with a fractional-order chaotic system: a novel and simple algorithm for analyzing residual arteriovenous access stenosis

Abstract

To detect the early developmental stages of arteriovenous access (AVA) stenosis in hemodialysis patients, this study explored a stenosis detector based on the Burg method and the fractional-order chaos system (FOCS). The bruit developed by the blood flowing through AVA can be a viable noninvasive strategy for monitoring AVA functions. We used the Burg method of the autoregressive model to estimate the frequency spectra of phonographic signals recorded by an electronic stethoscope in patients' AVAs and to identify the spectral peaks in the region of 25-800 Hz. The frequency spectra differed significantly between normal and stenosis statuses in AVA. We found that the frequency and amplitude in power spectra analysis varied in accordance with the severity of AVA stenosis. However, the correlation of these parameters for classifying the degree of stenosis is limited when only using the Burg method. Therefore, we used an FOCS to monitor the differing frequency spectra between the normal condition and AVA stenosis. The variances of these two conditions were dynamic errors that were the coupling variables that tracked the responses between the master system and the slave system. A total of 42 patients who had received percutaneous transluminal angioplasty (PTA) for their failing AVAs was recruited for this study. In this study, the dynamic error, Index Ψ, was used to calculate the frequency spectrum redistribution in patients undergoing PTA. In addition, ΔImp was the index used to evaluate improvements in the luminal diameter between pre- and post-PTA. Therefore, we used linear regression to model the relationship between ΔImp and Index Ψ. The findings indicate that the proposed method has enhanced efficiency, especially in the venous anastomosis (V-site). The FOCS is a novel and simple algorithm for analyzing the residual AVA stenosis of PTA treatment.

Arteriovenous Shunt Stenosis Evaluation Using a Fractional-Order Fuzzy Petri Net Based Screening System for Long-Term Hemodialysis Patients

“…In statistics, there is no statistical significance between the groups in arterial anastomosis sites (A-sites) and venous anastomosis sites (V-sites), p > 0.05. Therefore, the correlation between ∆DOS% and index Ψ at the V-sites is closer than for the A-sites, as shown in Table 1 [30]. We can use exponent regression to model the relationship between ∆DOS% and index Ψ, as well as DOS% and index Ψ, respectively.…”

“…A band pass filter with cut off at frequencies from 40 Hz and 800 Hz to prevent the base line from wandering and maintain the characteristic frequencies at the signal preprocessing stage [11] [12]. Frequency spectral evaluation was then used to find the characteristic frequencies using the Burg AR method with a given AR order of P = 8 [30]. The frequency region, <800 Hz, was chosen in this study.…”

Suppressing unsteady flow in arterio-venous fistulae

“…[81][82][83][84] It is well known that stenotic vessels produce highly unsteady flow; 26,77 spectra were adopted as a proxy for unsteadiness. Digital stethoscope recordings were made directly over the anastomosis both before (left-side AVF only) and after (left-side and right-side AVF) device implantation.…”

Non-Imaging Acoustical Properties in Monitoring Arteriovenous Hemodialysis Access. A Review

“…Some studies state that higher frequencies, in the range of 300 Hz and above, correlate with a higher degree of stenosis [11][12][13].Several analytical techniques have been applied to analyze these acoustic signal features to correlate them with the stages of the stenosis problem. These include root mean square (RMS), true RMS (TRMS), amplitude (A) [11], shorttime Fourier transform (STFT) and wavelet transform (WT) [13][14][15], artificial neural network (ANN) [16], Burg's method [17,18], fuzzy Petri net (FPN), probabilistic neural network (PNN) [12,19], and classifiers such as principal component analysis (PCA) [3], support vector machine (SVM) [3,20], and many others, as summarized in Table 1. They indicated that with these unique frequency features and their characteristics, normal and abnormal vascular sounds can be differentiated.…”

“…The use of digital signal processing techniques in the screening of fistula conditions has led to the development of a decision-making algorithm that allows identifying or classifying the stenosis or thrombosis in early conditions [12,32,33] and may also be used for continuous monitoring compared to imaging techniques [3,11,30]. The classification of AVA sounds is divided into two conditions: non-stenotic or stenotic, although some researchers propose sub-classes of the severity of stenotic cases [18]. Todo, et al analyzed the vascular shunt sound based on a Fourier time-frequency analysis on 50 subjects, and correlated this using two groups of features: brachial artery (FV) blood flow volume and resistance index (RI), to normal and stenotic fistulas.…”