You are here

Home ›

Biomedical Signal and Image Processing and Analysis for Diagnosis and Monitoring

Group Info

Our research group develops advanced methods for biomedical signal and image processing
to support diagnosis, prognosis, and continuous monitoring in healthcare.
We work at the interface of engineering, mathematics, and medicine,
translating complex physiological data into clinically meaningful markers.

The group combines expertise in biosignal processing, machine learning, artificial intelligence,
and medical device engineering to design, validate, and deploy data-driven diagnostic tools.

Research Focus

We study a broad spectrum of physiological signals and biomedical data, including:

  • Electroencephalography (EEG) for mental health, burnout, epilepsy, Parkinson’s disease, and brain–computer interfaces.
  • Electrocardiography (ECG) and heart rate variability (HRV) for arrhythmia, sleep apnea, and cardiovascular risk assessment.
  • Eye tracking and postural control signals for concussion, mild traumatic brain injury, and neurological disorders.
  • Electrical activity and action potentials of cardiac cells for cardiotoxicity assessment and drug safety studies.
  • Lung sound analysis for detection and monitoring of respiratory diseases such as bronchitis and COPD.

Methodologically, we focus on machine learning, deep learning, fractal and scaling analyses,
information-theoretic measures, and advanced time–frequency and nonlinear signal processing.

Societal Challenges & Impact

Our capacity addresses key societal challenges in healthcare by enabling:

  • Early detection and continuous monitoring of neurological, cardiac, and respiratory disorders.
  • Objective assessment of mental health, burnout, and cognitive workload in work and learning environments.
  • Improved diagnosis and follow-up of patients with concussion and mild traumatic brain injury, including war-related injuries.
  • Support for personalized medicine and decision support systems in hospitals and telemedicine settings.

Projects

EU-TRAINS

  • Full Title: EU-TRAINS
  • Acronym: EU-TRAINS
  • Funding Programme: Horizon Europe
  • Duration: 01.05.2024 – 30.04.2027
  • Website:
    https://eu-trains.eu/

Within EU-TRAINS we contribute expertise in biosignal analysis, AI-based diagnostics,
and data-driven methods for personalised healthcare and smart monitoring solutions.

Data & Research Infrastructure

The group maintains and develops curated databases of annotated biosignals, including:

  • EEG datasets (emotion perception, autistic spectrum disorder, cognitive workload, resting-state recordings).
  • ECG and HRV datasets for arrhythmia, ventricular late potentials, and sleep apnea studies.
  • Eye tracking, posture, and motion trajectories for balance, gait, and concussion assessment.
  • Electrical activity of cardiac cells, extracellular field potentials, and reconstructed action potentials.

These resources support the development, benchmarking, and validation of novel algorithms,
and are available for collaborative projects within ATHENA.

Members

Academic Staff & Researchers

  • Anton Popov – Group Leader, PhD, Professor – Google Scholar
  • Kateryna O. Ivanko – PhD, Associate Professor –
    Deputy Editor-in-Chief, Journal “Visnyk NTUU KPI Seriia – Radiotekhnika Radioaparatobuduvannia”;
    IEEE R8 Membership Development Committee Correspondent Member;
    Vice Chairperson of IEEE Ukraine Section Women in Engineering;
    Vice Chairperson of IEEE Ukraine Section (Kyiv) ED/MTT/CP/SSC Joint Chapter – Google Scholar
  • Yevhenii Karpliuk – PhD, Associate Professor – Google Scholar
  • Nataliia Ivanushkina – PhD, Associate Professor – Google Scholar
  • Hanna Porieva – PhD, Associate Professor; Chairperson of IEEE Ukraine Section (Kyiv) ED/MTT/CP/SSC Joint Chapter – Google Scholar

PhD Students

  • Anton Mnevets – Google Scholar
  • Viacheslav Bondarev – Google Scholar
  • Semen Mushta
  • Illya Mushta
  • Anton Kotsiubailo
  • Bohdan Kolomiets
  • Mykhailo Kudas
  • Danylo Yarosh
  • Danylo Reznichenko
  • Vitalii Ivashchuk
  • Dmytro Shevchuk

Selected Publications

    2025

  • Afek, N., Harmatiuk, D., Gawłowska, M., Ferreira, J.M.A., Golonka, K., Tukaiev, S., Popov, A., Marek, T. (2025). Functional connectivity in burnout syndrome: a resting-state EEG study. Frontiers in Human Neuroscience, 19:1481760. https://doi.org/10.3389/fnhum.2025.1481760
  • Mushta, I., Koks, S., Popov, A., Lysenko, O. (2025). Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning Approach. Bioengineering, 12(1), 11. https://doi.org/10.3390/bioengineering12010011
  • A. V. Mnevets, N. G. Ivanushkina, Classification of low-amplitude ECG components using adaptive activation functions of neural networks,
    Radioelectronics and Communications Systems, 2025. https://doi.org/10.20535/S0021347024120021
    Mnevets A.V., Ivanushkina N.G. Siamese neural network’s models for cardiac arrhythmia classification in the conditions of shortage of training ECG signals. Miscrosystems, Electronics and Acoustics, 2025, 30(2), p. 325111.1–325111.12, doi: 10.20535/2523-4455.mea.325111.

    • 2024

  • Mangalam M, Seleznov I, Kolosova E, Popov A, Kelty-Stephen DG and Kiyono K (2024) Postural control in gymnasts: anisotropic fractal scaling reveals proprioceptive reintegration in vestibular perturbation. Front. Netw. Physiol. 4:1393171. https://doi.org/10.3389/fnetp.2024.1393171
  • Mangalam, M., Kelty-Stephen, D.G., Seleznov, I., Popov, A., Likens, A.D., Kiyono, K., Stergiou, N. Older adults and individuals with Parkinson’s disease control posture along suborthogonal directions that deviate from the traditional anteroposterior and mediolateral directions. Sci Rep 14, 4117 (2024). https://doi.org/10.1038/s41598-024-54583-y
  • Lavrenko I, Popov A, Seleznov I, Kiyono K. Fractal Analysis of the Centrifuge Vibrograms. Fractal and Fractional. 2024; 8(1):60. https://doi.org/10.3390/fractalfract8010060
  • Danylo Reznichenko, Kateryna Ivanko, Nataliia Ivanushkina and Hanna Porieva “Analysis of Multichannel EEG Data and Heart Rate Variability for the Detection of Epileptic Seizures in Newborns”, 2024 IEEE 42st International Conference on Electronics and Nanotechnology (ELNANO), 2024, pp. 461-466. DOI: 10.1109/ELNANO63394.2024.10756852
  • Kateryna Ivanko, Hanna Porieva, Yevhenii Karpluk, Philip de Chazal, Orsolya Kekesi and Anton Popov “Heart Rate Variability in Normal Periods of Breathing in Sleep as a Marker of Obstructive Sleep Apnea Severity”, 2024 IEEE 42st International Conference on Electronics and Nanotechnology (ELNANO), 2024, pp. 467-472.
    DOI: 10.1109/ELNANO63394.2024.10756867
  • N. Ivanushkina, A. Mnevets, «Detection of Erroneously Selected Cardiac Cycles Using Neural Networks», 2024 IEEE 42nd International Conference on Electronics and Nanotechnology (ELNANO), 2024, pp. 406-411. DOI: 10.1109/ELNANO63394.2024.10756903
  • M. O. Shpotak, N. G. Ivanushkina, «Application of k-Nearest Neighbors Method for Drug Concentration and Cardiotoxicity Classification Using Extracellular Field Potentials and Reconstructed Action Potentials of Cardiac Cells», Miscrosystems, Electronics and Acoustics, 29(1), 2024, doi: 10.20535/2523-4455.2024.29.1
  • Mnevets A.V., Ivanushkina N.G. Neural Networks Detection of Low-Amplitude Components on ECG Using Modified Wavelet Transform. Visnyk NTUU KPI Seriia–Radiotekhnika Radioaparatobuduvannia, 2024, Iss.97, pp.46–57. https://radap.kpi.ua/radiotechnique/article/view/2001

    • 2023

  • Sandro Hurtado, José García-Nieto, Anton Popov, and Ismael Navas Delgado. 2023. "Human Activity Recognition From Sensorised Patient's Data in Healthcare: A Streaming Deep Learning-Based Approach," International Journal of Interactive Multimedia and Artificial Intelligence, 8(1), 23-37. http://dx.doi.org/10.9781/ijimai.2022.05.004
  • M. O. Shpotak, N. G. Ivanushkina, K. O. Ivanko, і Y. V. Prokopenko, «Estimation of Multiple Cardiac Cells’ Action Potentials From Extracellular Field Potentials», RADAP, вип. 93, с. 70–77, Вер 2023, doi: 10.20535/RADAP.2023.93.70-77.

    • 2022

  • Chernykh M, Vodianyk B, Seleznov I, Harmatiuk D, Zyma I, Popov A, Kiyono K. Detrending Moving Average, Power Spectral Density, and Coherence: Three EEG-Based Methods to Assess Emotion Irradiation during Facial Perception. Applied Sciences. 2022; 12(15):7849. https://doi.org/10.3390/app12157849
  • Y. Zerrouk, K. Ivanko, N. Ivanushkina, A. Korniienko, M. Basarab and H. Porieva, "Prediction of Epileptic Seizures Based on Analysis of Electrical Activity of the Brain and Parameters of Heart Rate Variability," 2022 IEEE 41st International Conference on Electronics and Nanotechnology (ELNANO), 2022, pp. 440-445, doi:10.1109/ELNANO54667.2022.9927003
  • Ivanushkina, N.G., Ivanko, K.O., Shpotak, M.O. and Prokopenko, Yu.V. Reconstruction of Action Potentials of Cardiac Cells from Extracellular Field Potentials. Radioelectron. Commun. Syst. 65, 354–364 (2022).
    https://link.springer.com/article/10.3103/S0735272722090047

    • 2021

  • O. Avilov, S. Rimbert, A. Popov and L. Bougrain, "Optimizing Motor Intention Detection With Deep Learning: Towards Management of Intraoperative Awareness," in IEEE Transactions on Biomedical Engineering, vol. 68, no. 10, pp. 3087-3097, Oct. 2021. https://doi.org/10.1109/TBME.2021.3064794
  • Fagan X., Ivanko K., Ivanushkina N. (2021) Detection of Ventricular Late Potentials in Electrocardiograms Using Machine Learning. In: Hu Z., Petoukhov S., Dychka I., He M. (eds) Advances in Computer Science for Engineering and Education. Advances in Intelligent Systems and Computing, vol 1247, pp 487–497. Springer, Cham. https://doi.org/10.1007/978-3-030-55506-1_44
  • Porieva H.S., Ivanko K.O., Semkiv C.I.,Vaityshyn V.I. Investigation of Lung Sounds Features for Detection of Bronchitis and COPD Using Machine Learning Methods/Visnyk NTUU KPI Seriia–Radiotekhnika Radioaparatobuduvannia, 2021, Iss.84, pp.78-87. http://radap.kpi.ua/radiotechnique/article/view/1710/1480
  • N. G. Ivanushkina, K. O. Ivanko, M. O. Shpotak, і Y. V. Prokopenko, Solving the Inverse Problem of Relationship Between Action Potentials and Field Potentials in Cardiac Cells, RADAP, вип. 85, с. 53–59, Чер 2021, https://doi.org/10.20535/RADAP.2021.85.53-59.

    • 2020

  • Seleznov, I., Popov, A., Kikuchi, K. et al. Detection of oriented fractal scaling components in anisotropic two-dimensional trajectories. Sci Rep 10, 21892 (2020). https://doi.org/10.1038/s41598-020-78807-z
  • Kotiuchyi, I.; Pernice, R.; Popov, A.; Faes, L.; Kharytonov, V. A Framework to Assess the Information Dynamics of Source EEG Activity and Its Application to Epileptic Brain Networks. Brain Sci. 2020, 10, 657. https://doi.org/10.3390/brainsci10090657
  • Kateryna Ivanko, Nataliia Ivanushkina, Anna Rykhalska. Identifying episodes of sleep apnea in ECG by machine learning methods/ Proceedings of 2020 IEEE 40th International Scientific Conference on Electronics and Nanotechnology. – 2020. – рp. 588 – 593.
    DOI: 10.1109/ELNANO50318.2020.9088749