Assessment of inflow and outflow stenoses using big spectral data and radial‐based colour relation analysis on in vitro arteriovenous graft biophysical experimental model

Chen, Weiling; Kan, Chung Dann; Lin, Chia‐Hung

doi:10.1049/iet-cps.2016.0040

Cited by 12 publications

(19 citation statements)

References 20 publications

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“…19 it is clear that all three recordings for the left-side AVF, before implanting the device, exhibit a peak at ∼600 Hz. This is in a similar range to peaks found by previous computational 42 and experimental 84 studies of flow in AVF configurations and is presumed to be caused by the AVF junction (although to be sure one should compare with PSD of the native vasculature, prior to AVF creation, which were not available). From Fig.…”

Section: Stethoscope Recordingsupporting

confidence: 76%

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Suppressing unsteady flow in arterio-venous fistulae

et al. 2017

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Arterio-Venous Fistulae (AVF) are regarded as the “gold standard” method of vascular access for patients with end-stage renal disease who require haemodialysis. However, a large proportion of AVF do not mature, and hence fail, as a result of various pathologies such as Intimal Hyperplasia (IH). Unphysiological flow patterns, including high-frequency flow unsteadiness, associated with the unnatural and often complex geometries of AVF are believed to be implicated in the development of IH. In the present study, we employ a Mesh Adaptive Direct Search optimisation framework, computational fluid dynamics simulations, and a new cost function to design a novel non-planar AVF configuration that can suppress high-frequency unsteady flow. A prototype device for holding an AVF in the optimal configuration is then fabricated, and proof-of-concept is demonstrated in a porcine model. Results constitute the first use of numerical optimisation to design a device for suppressing potentially pathological high-frequency flow unsteadiness in AVF.

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Section: Stethoscope Recordingsupporting

confidence: 76%

“…[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.…”

Section: Stethoscope Recordingmentioning

confidence: 99%

Suppressing unsteady flow in arterio-venous fistulae

et al. 2017

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“…According to the HSV color model, (13)(14)(15) the CRA model is defined as a transformation manner from the similarity degrees to the HSV color space. As seen in Fig.…”

Section: Radial-based Cramentioning

confidence: 99%

“…The characteristic features are extracted using the FFT method, including the frequencies 1 × f, 2 × f, 3 × f, ..., and 9 × f, where the frequency f is 60 Hz. To develop an assistant tool, the frequency spectral data analysis and radial-based color relation analysis (CRA) (12)(13)(14)(15) are carried out with a machine learning model (a) (b) to monitor the mechanical vibration faults. Among multidimensional big data, the radial-based function is a kernel model with Gaussian functions to measure dissimilarity and similarity degrees, which are parameterized into the specific membership grades for pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Mechanical Vibration Fault Detection for Turbine Generator Using Frequency Spectral Data and Machine Learning Model: Feasibility Study of Big Data Analysis

2018

Sensors and Materials

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Keywords: vibration signal, mechanical vibration fault, frequency spectral data, radial-based color relation analysisThe frequency spectra of vibration signals can be used to monitor the mechanical conditions of a turbine generator. Frequency-based features are extracted by fast Fourier transformation (FFT). The changes in frequency spectral data and amplitude are used to separate the normal condition from the fault conditions. These features indicate that the characteristic frequencies are 1 × f, 2 × f, 3 × f, and two other frequency bands, < 0.4 × f and > 3 × f, where the frequency f is the rotor frequency. The power spectral data shows the mechanical vibration fault at particular characteristic frequencies. Then, radial-based color relation analysis (CRA) is applied to identify mechanical faults, including normal condition, oil-membrane oscillation, imbalance, and no orderliness. Using practical records, the experimental results will show that the proposed method has a higher accuracy in mechanical vibration fault detection.

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“…Then, a machine learning-based decision-making method utilises a screening method to automatically detect HD patients with or without the risk of PAD. Radial-based colour relation analysis (CRA) [26] is employed to parameterise the similarity and dissimilarity levels using the fuzzification operations, and then CRA utilises the membership grades into the perceptual colour relationships for PAD screening.…”

Section: Introductionmentioning

confidence: 99%