Optimal flow adjustment of veno‐venoarterial extracorporeal membrane oxygenation with an adaptive prediction model: cannula sizes screening and pump speeds estimation

Kan, Chung Dann; Chen, Wei Ling; Lin, Chia Hung; Lu, Peng; Du, Yi

doi:10.1049/iet-smt.2015.0030

Cited by 8 publications

(12 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, each pair of AUSP ratios, ρ R and ρ L , can be replaced by the substitution rates, such as the parameters, λ 1 and λ 2 . For two states, the concept of substitution‐rate matrix, Q , is defined as follows [24, 26]:

\begin{matrix} right leftthickmathspace.5em \\ Q & = [\begin{matrix} ρ_{11} & ρ_{12} \\ ρ_{21} & ρ_{22} \end{matrix}] \\ = [\begin{matrix} - ρ_{R} & ρ_{R} \\ ρ_{L} & - ρ_{L} \end{matrix}] = U Λ {bold-italicU}^{- 1}, ρ_{12} = - ρ_{11}, ρ_{21} = - ρ_{22} \end{matrix}

false\Rightarrow bold-italicQ = bold-italicU boldΛ U^{- 1} = (][, \begin{matrix} 1em4pt \\ u_{11} & u_{12} \\ u_{21} & u_{22} \end{matrix}) (][, \begin{matrix} 1em4pt \\ λ_{1} & 0 \\ 0 & λ_{2} \end{matrix}) {[\begin{matrix} u_{11} & u_{12} \\ u_{21} & u_{22} \end{matrix}]}^{- 1}

where each raw element of the matrix, Q , sums to 0 (− ρ R + ρ R = 0 and ρ L − ρ L = 0); matrix, U , is a non‐singular matrix and U −1 is its inverse; Λ is a diagonal matrix, diag{ λ 1 , λ 2 }; and λ 1 and λ 2 are the eigenvalues of matrix Q .…”

Section: Methodsmentioning

confidence: 99%

“…Hence, the ideal transition probability is

\begin{matrix} right leftthickmathspace.5em \\ \Rightarrow P_{ideal} (t) & = [\begin{matrix} P_{11, ideal} & P_{12, ideal} \\ P_{21, ideal} & P_{22, ideal} \end{matrix}] \\ = [\begin{matrix} 0.5 e^{- 2 t} + 0.5 & - 0.5 e^{- 2 t} + 0.5 \\ - 0.5 e^{- 2 t} + 0.5 & 0.5 e^{- 2 t} + 0.5 \end{matrix}] \end{matrix}

We can then apply a column vector [ P 11 ( t ) P 22 ( t )] T to estimate sequence similarity with the pair of AUSP ratios, ρ R and ρ L . The Euclidean distance estimation is used to screen the similarity, as follows [26]:

d_{R} = \sqrt{{false\sum}_{n = 1}^{N} {false(P_{11} false(n false) - P_{11, ideal} false(n false) false)}^{2}}

d_{L} = \sqrt{{false\sum}_{n = 1}^{N} {false(P_{22} false(n false) - P_{22, ideal} false(n false) false)}^{2}}

where N is the number of sampling points within infinitely small time intervals, Δ t = t RL / N , n = 1, 2, 3,…, N . In time interval t RL , the joint probability accommodating the variance with AUSP ratios, ρ R and ρ L , is as follows:

\begin{matrix} right leftthickmathspace.5em \\ p_{R L} (t_{R L}) & = p_{R} (t_{R L}) \times p_{L} \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Peripheral arterial disease screening for hemodialysis patients using a fractional‐order integrator and transition probability decision‐making model

Chen

et al. 2017

IET syst. biol.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

\begin{matrix} right leftthickmathspace.5em \\ Q & = [\begin{matrix} ρ_{11} & ρ_{12} \\ ρ_{21} & ρ_{22} \end{matrix}] \\ = [\begin{matrix} - ρ_{R} & ρ_{R} \\ ρ_{L} & - ρ_{L} \end{matrix}] = U Λ {bold-italicU}^{- 1}, ρ_{12} = - ρ_{11}, ρ_{21} = - ρ_{22} \end{matrix}

false\Rightarrow bold-italicQ = bold-italicU boldΛ U^{- 1} = (][, \begin{matrix} 1em4pt \\ u_{11} & u_{12} \\ u_{21} & u_{22} \end{matrix}) (][, \begin{matrix} 1em4pt \\ λ_{1} & 0 \\ 0 & λ_{2} \end{matrix}) {[\begin{matrix} u_{11} & u_{12} \\ u_{21} & u_{22} \end{matrix}]}^{- 1}

Section: Methodsmentioning

confidence: 99%

“…Hence, the ideal transition probability is

\begin{matrix} right leftthickmathspace.5em \\ \Rightarrow P_{ideal} (t) & = [\begin{matrix} P_{11, ideal} & P_{12, ideal} \\ P_{21, ideal} & P_{22, ideal} \end{matrix}] \\ = [\begin{matrix} 0.5 e^{- 2 t} + 0.5 & - 0.5 e^{- 2 t} + 0.5 \\ - 0.5 e^{- 2 t} + 0.5 & 0.5 e^{- 2 t} + 0.5 \end{matrix}] \end{matrix}

d_{R} = \sqrt{{false\sum}_{n = 1}^{N} {false(P_{11} false(n false) - P_{11, ideal} false(n false) false)}^{2}}

d_{L} = \sqrt{{false\sum}_{n = 1}^{N} {false(P_{22} false(n false) - P_{22, ideal} false(n false) false)}^{2}}

\begin{matrix} right leftthickmathspace.5em \\ p_{R L} (t_{R L}) & = p_{R} (t_{R L}) \times p_{L} \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

Peripheral arterial disease screening for hemodialysis patients using a fractional‐order integrator and transition probability decision‐making model

Chen

et al. 2017

IET syst. biol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…CRA utilises the membership grades into ‘hue angle’ and ‘saturation value’ to identify the DOS by describing the perceptual colour relationships for DOS < 50%, 50% < DOS < 70%, and DOS > 70% screening. In contrast to the multiple linear regression [23, 24] and machine learning methods [25, 26], the proposed model overcomes the three limitations, including the determination of multilayer network structure, the choices of optimisation methods, and the update of parameters with iterative computations.…”

Section: Introductionmentioning

confidence: 99%

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

Kan

Lin

2017

IET cyber-phys. syst.

Self Cite

View full text Add to dashboard Cite

“…Its configuration could be determined using the presentation of 16 input–output pairs of training patterns [ 17 , 18 ]. We had four input nodes in the input layer, 16 pattern nodes in the pattern layer, four nodes in the summation layer, and three nodes in the output layer (network topology: 4-16-4-3).…”

Section: Resultsmentioning

confidence: 99%

Blood leakage detection during dialysis therapy based on fog computing with array photocell sensors and heteroassociative memory model

Huang

Lin

et al. 2018

Healthcare Technology Letters

Self Cite

View full text Add to dashboard Cite

Blood leakage and blood loss are serious life-threatening complications occurring during dialysis therapy. These events have been of concerns to both healthcare givers and patients. More than 40% of adult blood volume can be lost in just a few minutes, resulting in morbidities and mortality. The authors intend to propose the design of a warning tool for the detection of blood leakage/blood loss during dialysis therapy based on fog computing with an array of photocell sensors and heteroassociative memory (HAM) model. Photocell sensors are arranged in an array on a flexible substrate to detect blood leakage via the resistance changes with illumination in the visible spectrum of 500–700 nm. The HAM model is implemented to design a virtual alarm unit using electricity changes in an embedded system. The proposed warning tool can indicate the risk level in both end-sensing units and remote monitor devices via a wireless network and fog/cloud computing. The animal experimental results (pig blood) will demonstrate the feasibility.

show abstract

Optimal flow adjustment of veno‐venoarterial extracorporeal membrane oxygenation with an adaptive prediction model: cannula sizes screening and pump speeds estimation

Cited by 8 publications

References 20 publications

Peripheral arterial disease screening for hemodialysis patients using a fractional‐order integrator and transition probability decision‐making model

Peripheral arterial disease screening for hemodialysis patients using a fractional‐order integrator and transition probability decision‐making model

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

Blood leakage detection during dialysis therapy based on fog computing with array photocell sensors and heteroassociative memory model

Contact Info

Product

Resources

About