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Roberto Leonarduzzi

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JMLR Journal 2020 Journal Article

Kymatio: Scattering Transforms in Python

  • Mathieu Andreux
  • Tomás Angles
  • Georgios Exarchakis
  • Roberto Leonarduzzi
  • Gaspar Rochette
  • Louis Thiry
  • John Zarka
  • Stéphane Mallat

The wavelet scattering transform is an invariant and stable signal representation suitable for many signal processing and machine learning applications. We present the Kymatio software package, an easy-to-use, high-performance Python implementation of the scattering transform in 1D, 2D, and 3D that is compatible with modern deep learning frameworks, including PyTorch and TensorFlow/Keras. The transforms are implemented on both CPUs and GPUs, the latter offering a significant speedup over the former. The package also has a small memory footprint. Source code, documentation, and examples are available under a BSD license at https://www.kymat.io. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2020. ( edit, beta )

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JBHI Journal 2017 Journal Article

Sparse Support Vector Machine for Intrapartum Fetal Heart Rate Classification

  • Jiri Spilka
  • Jordan Frecon
  • Roberto Leonarduzzi
  • Nelly Pustelnik
  • Patrice Abry
  • Muriel Doret

Fetal heart rate (FHR) monitoring is routinely used in clinical practice to help obstetricians assess fetal health status during delivery. However, early detection of fetal acidosis that allows relevant decisions for operative delivery remains a challenging task, receiving considerable attention. This contribution promotes sparse support vector machine classification that permits to select a small number of relevant features and to achieve efficient fetal acidosis detection. A comprehensive set of features is used for FHR description, including enhanced and computerized clinical features, frequency domain, and scaling and multifractal features, all computed on a large (1288 subjects) and well-documented database. The individual performance obtained for each feature independently is discussed first. Then, it is shown that the automatic selection of a sparse subset of features achieves satisfactory classification performance (sensitivity 0. 73 and specificity 0. 75, outperforming clinical practice). The subset of selected features (average depth of decelerations MAD dtrd, baseline level β 0, and variability H) receives simple interpretation in clinical practice. Intrapartum fetal acidosis detection is improved in several respects: A comprehensive set of features combining clinical, spectral, and scale-free dynamics is used; an original multivariate classification targeting both sparse feature selection and high performance is devised; state-of-the-art performance is obtained on a much larger database than that generally studied with description of common pitfalls in supervised classification performance assessments.

Details DOI
v2026.09.13