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S. Van Huffel

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8 papers
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8

JBHI Journal 2017 Journal Article

Nonnegative Canonical Polyadic Decomposition for Tissue-Type Differentiation in Gliomas

  • H. N. Bharath
  • D. M. Sima
  • N. Sauwen
  • U. Himmelreich
  • L. De Lathauwer
  • S. Van Huffel

Magnetic resonance spectroscopic imaging (MRSI) reveals chemical information that characterizes different tissue types in brain tumors. Blind source separation techniques are used to extract the tissue-specific profiles and their corresponding distribution from the MRSI data. We focus on automatic detection of the tumor, necrotic and normal brain tissue types by constructing a 3D MRSI tensor from in vivo 2D-MRSI data of individual glioma patients. Nonnegative canonical polyadic decomposition (NCPD) is applied to the MRSI tensor to differentiate various tissue types. An in vivo study shows that NCPD has better performance in identifying tumor and necrotic tissue type in glioma patients compared to previous matrix-based decompositions, such as nonnegative matrix factorization and hierarchical nonnegative matrix factorization.

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JBHI Journal 2014 Journal Article

An Evaluation of Cardiorespiratory and Movement Features With Respect to Sleep-Stage Classification

  • T. Willemen
  • D. Van Deun
  • V. Verhaert
  • M. Vandekerckhove
  • V. Exadaktylos
  • J. Verbraecken
  • S. Van Huffel
  • B. Haex

Polysomnography (PSG) is considered the gold standard to assess sleep accurately, but it can be expensive, time-consuming, and uncomfortable, specifically in long-term sleep studies. Actigraphy, on the other hand, is both cheap and user-friendly, but depending on the application lacks detail and accuracy. Our aim was to evaluate cardiorespiratory and movement signals in discriminating between wake, rapid-eye-movement (REM), light (N1N2), and deep (N3) sleep. The dataset comprised 85 nights of PSG from a healthy population. Starting from a total of 750 characteristic variables (features), problem-specific subsets of 40 features were forwardly selected using the combination of a wrapper method (Cohen's kappa statistic on radial basis function (RBF)-kernel support vector machine (SVM) classifier) and filter method (minimum redundancy maximum relevance criterion on mutual information). Final classification was performed using an RBF-kernel SVM. Non-subject-specific wake versus sleep classification resulted in a Cohen's kappa value of 0. 695, while REM versus NREM resulted in 0. 558 and N3 versus N1N2 in 0. 553. The broad pool of initial features gave insight in which features discriminated best between the different classes. The classification results demonstrate the possibility of making long-term sleep monitoring more widely available.

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