@INPROCEEDINGS{7318771,
  author={R. {Leonarduzzi} and J. {Spilka} and J. {Frecon} and H. {Wendt} and N. {Pustelnik} and S. {Jaffard} and P. {Abry} and M. {Doret}},
  booktitle={2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, 
  title={P-leader multifractal analysis and sparse SVM for intrapartum fetal acidosis detection}, 
  year={2015},
  volume={},
  number={},
  pages={1971-1974},
  abstract={Interpretation and analysis of intrapartum fetal heart rate, enabling early detection of fetal acidosis, remains a challenging signal processing task. Among the many strategies that were used to tackle this problem, scale-invariance and multifractal analysis stand out. Recently, a new and promising variant of multifractal analysis, based on p-leaders, has been proposed. In this contribution, we use sparse support vector machines applied to p-leader multifractal features with a double aim: Assessment of the features actually contributing to classification; Assessment of the contribution of non linear features (as opposed to linear ones) to classification performance. We observe and interpret that the classification rate improves when small values of the tunable parameter p are used.},
  keywords={diseases;medical signal processing;obstetrics;signal classification;support vector machines;p-leader multifractal analysis;sparse SVM;intrapartum fetal acidosis detection;intrapartum fetal heart rate;early detection;signal processing task;scale-invariance;sparse support vector machines;classification performance;tunable parameter;Fractals;Fetal heart rate;Support vector machines;Correlation;Databases;Wavelet transforms;Estimation;Acidosis;Area Under Curve;Female;Fetal Diseases;Heart Rate, Fetal;Humans;Linear Models;Multivariate Analysis;Pregnancy;ROC Curve;Signal Processing, Computer-Assisted;Support Vector Machine},
  doi={10.1109/EMBC.2015.7318771},
  ISSN={1558-4615},
  month={Aug},}