Milena B. Cukic Radenkovic
Orcid: 0000-0002-9162-987XAffiliations:
- Complutense University of Madrid, Spain
- University of Belgrade, Department for General Physiology and Biophysics, Serbia
- Clinical Center Serbia, Institute for Neurology, Belgrade, Serbia
According to our database1,
Milena B. Cukic Radenkovic
authored at least 12 papers
between 2013 and 2023.
Collaborative distances:
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Bibliography
2023
Frontiers Digit. Health, March, 2023
Linear and Non-Linear Heart Rate Variability Indexes from Heart-Induced Mechanical Signals Recorded with a Skin-Interfaced IMU.
Sensors, February, 2023
2022
Heart Rate And Heart Rate Variability Indexes Estimated By Mechanical Signals From A Skin-Interfaced IMU.
Proceedings of the IEEE International Workshop on Metrology for Industry 4.0 & IoT, 2022
An Unexpected Connection from Our Personalized Medicine Approach to Bipolar Depression Forecasting.
Proceedings of the Intelligent Systems and Applications, 2022
2021
Discussion on Y. Zhu, X. Wang, K. Mathiak, P. Toiviainen, T. Ristaniemi, J. Xu, Y. Chang and F. Cong, Altered EEG Oscillatory Brain Networks During Music-Listening in Major Depression, International Journal of Neural Systems, Vol. 31 No. 3 (2021)
Int. J. Neural Syst., 2021
2020
On mistakes we made in prior Computational Psychiatry Data driven approach projects and how they jeopardize translation of those findings in clinical practice.
CoRR, 2020
On Mistakes We Made in Prior Computational Psychiatry Data Driven Approach Projects and How They Jeopardize Translation of Those Findings in Clinical Practice.
Proceedings of the Intelligent Systems and Applications, 2020
2019
Machine Learning Approaches for Detecting the Depression from Resting-State Electroencephalogram (EEG): A Review Study.
CoRR, 2019
Machine learning approaches in Detecting the Depression from Resting-state Electroencephalogram (EEG): A Review Study.
CoRR, 2019
2018
EEG machine learning with Higuchi fractal dimension and Sample Entropy as features for successful detection of depression.
CoRR, 2018
2013
Identification of the Long-Term Effects of Mild to Moderate Neonatal Cerebral Hypoxia Based on EEG Signals Analysis.
Simul. Notes Eur., 2013