You can find updated information at this web page.
Researcher at Czech Institute of Informatics, Robotics and Cybernetics (CIIRC)
Czech Technical University in Prague
biomedical signal processing, machine learning, classification, fetal heart rate analysis and classification, cardiotocography
Past research projects:(Grant IGA) – Cardiotocography evaluation by means of artificial intelligence |
(Eniac-MAS) – One part of the project: mCTG – Mobile CardiTocoGraphy – telemedicine application for risk pregnancies at home. |
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P. Abry, J. Spilka, R. Leonarduzzi, V. Chudáček, N. Pustelnik, M. Doret
Sparse learning for Intrapartum fetal heart rate analysis
In Biomedical Physics Engineering Express 4(3) 034002, 2018.
web | .pdf | .bib | paper results |
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J. Spilka, J. Frecon, R. Leonarduzzi, N. Pustelnik, P. Abry, M. Doret
Sparse Support Vector Machine for Intrapartum Fetal Heart Rate Classification
In IEEE Journal of Biomedical and Health Informatics, 21(3), 664 - 671, 2017.
web | .pdf | .bib |
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M. Doret, J. Spilka, V. Chudáček, P. Goncalves, P. Abry
Fractal Analysis and Hurst Parameter for Intrapartum Fetal Heart Rate Variability
Analysis:
A Versatile Alternative to Frequency Bands and LF/HF Ratio
In PLoS ONE, vol. 10, no. 8, p. e0136661, 08 2015. web | .pdf | .bib |
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J. Spilka, J. Frecon, R. Leonarduzzi, N. Pustelnik, P. Abry, M. Doret
Intrapartum Fetal Heart Rate Classification from Trajectory in Sparse SVM feature
space
In Ann Int Conf IEEE Eng Med Biol Soc (EMBC), Milan, Italy, August 2015, pp.
2335--2338. web | .pdf | .bib |
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L. Hruban, J. Spilka, V. Chudáček, P. Janků, et al.
Agreement on intrapartum cardiotocogram recordings between expert obstetricians
In Journal of Evaluation in Clinical Practice, 21(4): 694-702, 2015. web | .pdf | .bib |
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P. Karvelis, J. Spilka, G. Georgoulas, V. Chudáček, C. Stylios, L. Lhotská
Combining Latent Class Analysis Labeling with Multiclass Approach for Fetal Heart Rate
Categorization
In Physiological Measurement 36, 1001-1024, 2015. web | .pdf | .bib |
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J. Spilka, S.G. Roux, N.B. Garnier, P. Abry, P. Goncalves, and M. Doret
Nearest-neighbor based wavelet entropy rate measures for intrapartum fetal heart rate
variability
In Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International
Conference of the IEEE, 2813–2816, IEEE, 2014. web | .pdf | .bib |
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J. Spilka, V. Chudáček, P. Janků, L. Hruban, M. Burša, M. Huptych, L. Zach, L. Lhotská.
Analysis of obstetricians’ decision making on CTG recordings.
Journal of Biomedical Informatics 2014, 51:72-79, 2014 web | .pdf | .bib |
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V. Chudáček, J. Spilka, M. Burša, P. Janků, L. Hruban, M. Huptych, L. Lhotská.
Open access intrapartum CTG database.
BMC Pregnancy and Childbirth 2014 14(1):16, 2014. web | .pdf | .bib |
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J. Spilka, V. Chudáček, M. Koucký, L. Lhotská, M. Huptych, P. Janků, G. Georgoulas, C. Stylios.
Using nonlinear features for fetal heart rate classification.
Biomedical Signal Processing and Control 7(4):350–357, 2012. web | .pdf | .bib |
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V. Chudáček, J. Spilka, P. Janků, M. Koucký, L. Lhotská, and M. Huptych.
Automatic evaluation of intrapartum fetal heart rate recordings:
A comprehensive analysis of useful features.
Physiological Measurement 32:1347–1360, 2011. web | .pdf | .bib |
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J. Spilka, V. Chudáček, V. Kužílek, L. Lhotská, and M. Hanuliak.
Detection of Inferior Myocardial Infarction: A Comparison of Various Decision Systems
and Learning Algorithms.
In Computers in Cardiology volume 37, 2010 web | .pdf | .bib |
present: Artificial
Intelligence
past: Medical Informatics,
Introduction to Biomedical
Engineering,
eHealth and telemedicine