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Low-Dimensional Stochastic Modeling of the Electrical Properties of Biological Tissues

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Ulrich Römer, Christian Schmidt, Ursula van Rienen, Sebastian Schöps

Computational Engineering, Finance, and Science · Submitted 22 Nov 2016

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim It is shown how the number of parameters can be significantly reduced by the Karhunen-Loeve expansion.

how it is stated · asserted 1.00 read the record →

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arXiv ID1611.07403 (version 2)
Pith IDZI2PTEXS, Pith's permanent record of this paper and its review
CitationChristian Schmidt, Sebastian Sch\"ops, Ulrich R\"omer, Ursula van Rienen. "Low-Dimensional Stochastic Modeling of the Electrical Properties of Biological Tissues." arXiv:1611.07403 (2016). https://pith.science/paper/ZI2PTEXS
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,760 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.24 · human participants 0.09 · controlled comparison 0.03 · randomized assignment 0.01 · states quantitative result 0.10 · compares with prior results 0.16 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

Uncertainty quantification plays an important role in biomedical engineering as measurement data is often unavailable and literature data shows a wide variability. Using state-of-the-art methods one encounters difficulties when the number of random inputs is large. This is the case, e.g., when using composite Cole-Cole equations to model random electrical properties. It is shown how the number of parameters can be significantly reduced by the Karhunen-Loeve expansion. The low-dimensional random model is used to quantify uncertainties in the axon activation during deep brain stimulation. Numerical results for a Medtronic 3387 electrode design are given.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Uncertainty quantification plays an important role in biomedical engineering as measurement data is often unavailable and literature data shows a wide variability. background 1.00 0.06 0.03 asserted 1.00
s1 Using state-of-the-art methods one encounters difficulties when the number of random inputs is large. background 0.76 0.13 0.03 asserted 0.98
s2 This is the case, e.g., when using composite Cole-Cole equations to model random electrical properties. background 0.94 0.10 0.03 asserted 0.82
s3 It is shown how the number of parameters can be significantly reduced by the Karhunen-Loeve expansion. method 0.68 0.88 0.40 asserted 1.00
s4 The low-dimensional random model is used to quantify uncertainties in the axon activation during deep brain stimulation. contribution 0.67 0.90 0.33 asserted 1.00
s5 Numerical results for a Medtronic 3387 electrode design are given. result 0.78 0.88 0.07 asserted 0.98

What kind of paper the classifier reports this is: method or tool 0.89 · evidence from simulation or computation 1.00.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Uncertainty quantification plays an important role in biomedical engineering as measurement data is often unavailable and literature data shows a wide variability. Using state-of-the-art methods one encounters difficulties when the number of random inputs is large. This is the case, e.g., when using composite Cole-Cole equations to model random electrical properties. It is shown how the number of parameters can be significantly reduced by the Karhunen-Loeve expansion. The low-dimensional random model is used to quantify uncertainties in the axon activation during deep brain stimulation. Numerical results for a Medtronic 3387 electrode design are given.

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