P-leader multifractal analysis and sparse SVM for intrapartum fetal acidosis detection

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F15%3A00239725" target="_blank" >RIV/68407700:21730/15:00239725 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/xpl/abstractKeywords.jsp?tp=&arnumber=7318771&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel7%2F7302811%2F7318236%2F07318771.pdf%3Farnumber%3D7318771" target="_blank" >http://ieeexplore.ieee.org/xpl/abstractKeywords.jsp?tp=&arnumber=7318771&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel7%2F7302811%2F7318236%2F07318771.pdf%3Farnumber%3D7318771</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/EMBC.2015.7318771" target="_blank" >10.1109/EMBC.2015.7318771</a>

Result language
angličtina
Original language name
P-leader multifractal analysis and sparse SVM for intrapartum fetal acidosis detection
Original language description
IInterpretation 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 ofthe 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.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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