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- Johansson, A., et al.
(författare)
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Adaptive finite element solution of multiscale PDE-ODE systems
- 2015
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Ingår i: Computer Methods in Applied Mechanics and Engineering. - : Elsevier BV. - 0045-7825 .- 1879-2138. ; 287, s. 150-171
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Tidskriftsartikel (refereegranskat)abstract
- We consider adaptive finite element methods for a multiscale system consisting of a macroscale model comprising a system of reaction-diffusion partial differential equations coupled to a microscale model comprising a system of nonlinear ordinary differential equations. A motivating example is modeling the electrical activity of the heart taking into account the chemistry inside cells in the heart. Such multiscale models are computationally challenging due to the multiple scales in time and space that are involved. We describe a mathematically consistent approach to couple the microscale and macroscale models based on introducing an intermediate "coupling scale". Since the ordinary differential equations are defined on a much finer spatial scale than the finite element discretization for the partial differential equation, we introduce a Monte Carlo approach to sampling the fine scale ordinary differential equations. We derive goal-oriented a posteriori error estimates for quantities of interest computed from the solution of the multiscale model using adjoint problems and computable residuals. We distinguish the errors in time and space for the partial differential equation and the ordinary differential equations separately and include errors due to the transfer of the solutions between the equations. The estimate also includes terms reflecting the sampling of the microscale model. Based on the accurate error estimates, we devise an adaptive solution method using a "blockwise" approach. The method and estimates are illustrated using a realistic problem.
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- Neuwirth, Christoph, et al.
(författare)
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Implementing Motor Unit Number Index (MUNIX) in a large clinical trial : Real world experience from 27 centres
- 2018
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Ingår i: Clinical Neurophysiology. - : Elsevier. - 1388-2457 .- 1872-8952. ; 129:8, s. 1756-1762
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: Motor Unit Number Index (MUNIX) is a quantitative neurophysiological method that reflects loss of motor neurons in Amyotrophic Lateral Sclerosis (ALS) in longitudinal studies. It has been utilized in one natural history ALS study and one drug trial (Biogen USA) after training and qualification of raters.METHODS: Prior to testing patients, evaluators had to submit test-retest data of 4 healthy volunteers. Twenty-seven centres with 36 raters measured MUNIX in 4 sets of 6 different muscles twice. Coefficient of variation of all measurements had to be <20% to pass the qualification process. MUNIX COV of the first attempt, number of repeated measurements and muscle specific COV were evaluated.RESULTS: COV varied considerably between raters. Mean COV of all raters at the first measurements was 12.9% ± 13.5 (median 8.7%). Need of repetitions ranged from 0 to 43 (mean 10.7 ± 9.1, median 8). Biceps and first dorsal interosseus muscles showed highest repetition rates. MUNIX variability correlated considerably with variability of compound muscle action potential.CONCLUSION: MUNIX revealed generally good reliability, but was rater dependent and ongoing support for raters was needed.SIGNIFICANCE: MUNIX can be implemented in large clinical trials as an outcome measure after training and a qualification process.
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- Xia, Xiaojuan, et al.
(författare)
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Selvester scoring in patients with strict LBBB using the QUARESS software.
- 2015
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Ingår i: Journal of Electrocardiology. - : Elsevier BV. - 1532-8430 .- 0022-0736. ; 48:5, s. 763-768
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Tidskriftsartikel (refereegranskat)abstract
- Estimation of the infarct size from body-surface ECGs in post-myocardial infarction patients has become possible using the Selvester scoring method. Automation of this scoring has been proposed in order to speed-up the measurement of the score and improving the inter-observer variability in computing a score that requires strong expertise in electrocardiography. In this work, we evaluated the quality of the QuAReSS software for delivering correct Selvester scoring in a set of standard 12-lead ECGs.
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