Recent projects

The Integrated Health Solutions – Health Ukraine (IHS-HU)

A web-portal focused on R&D, human capital, organizations and contacts from the Ukrainian life science and health sector (under

MeCo, a free 2 year research study developed by IHS-HU

with the goal of understanding the progression of Parkinson’s disease that appears to be unique to individuals.

Portable noninvasive ventilation system for pulmonary rehabilitation

(State registration number 0119U103758)


The research aimed to improving data analysis technique to find dependences between the individual factors isolated pregnancy and the presence of the disease in the newborn, discovering interesting associations in the gestation course data, as well as obtaining new grouping factors for the data.

NEO-Healthy is a risk-scoring tool to identify pregnant women with high risk of fetal and newborn hypoxia.

The first implementation of NEO-Healthy was NEOMed (2017). After two years of data gathering, digitalization and pre-processing we applied a series of data mining technique to identify meaningful factors of hypoxic fetal injuries and developed a risk score tool for fetal hypoxia. The process of risk estimation relies on data from lab testing, biometric data, claims, patient-generated health data, and the social determinants of health. We use the predictive modelling technique to identify proactively patients with highest risk of the fetal pathology.

The NEO-Healthy is a further enhancement of NEOMed. We use non-invasive fetal heart monitoring (fHR) to detect abnormal fHR patterns that may indicate oxygen deficiency or other pathological processes at their early stage to perform appropriate intervention and prevent fetal death and damage.

Our current goal is to automate the detection of the risk of fetal hypoxia through fECG monitoring derived from the signal recorded from the maternal abdominal surface. To solve this task, we propose a novel method for automatic electrocardiogram (fECG) signal processing and hypoxia risk estimation. To the best of our knowledge, this is the first attempt to introduce the auto-generated risk scoring in fECG. We assume that risk evaluation allows achieving early warning on fetus’ non-reassuring fetal status and provide physician with additional information about the possible fetal complications.


  1. I. Skarga-Bandurova, T. Biloborodova, M. Nesterov. Extracting Interesting Rules from Gestation Course Data for Early Diagnosis of Neonatal Hypoxia. Journal of Medical Systems (2019) 43:8  Springer US.
  2. I. Skarga-Bandurova, T. Biloborodova, I. Skarha-Bandurov, N. Zagorodna, L. Shumova EEG Data Fusion for Improving Accuracy of Binary Classification. ICT for Health Science Research A. Shabo (Shvo) et al. (Eds.) 2019. Vol. 258. pp. 130-134.
  3. I. Skarga-Bandurova, T.Biloborodova, and Y. Dyachenko Strategy to Managing Mixed Datasets with Missing Items, In: J. Medinaetal. (eds.) Information Processing and Management of Uncertainty in Knowledge-Based Systems – Theory and Foundations. IPMU 2018, Communications in Computer and Information Science, vol. 854. pp. 608–620, 2018. Springer, Cham.
  4. I. Skarga-Bandurova, T. Biloborodova, and M. Nesterov Discovering Interesting Associations in Gestation Course Data, Progress in Artificial Intelligence. EPIA 2017. Oliveira E. et al. (Eds): Lecture Notes in Computer Science. – 2017. – vol 10423. –  pp. 204-214. Springer, Cham.
  5. T. Biloborodova and I. Skarga-Bandurova Approaches for Classification of Imbalanced and Skewed Datasets, Herald of East Ukrainian National University. – Severodonetsk: EUNU. – No. 8 (238), pages 17-24, 2017.
  6. I. Skarga-Bandurova and T. Biloborodova Exploratory Data Analysis to Identifying Meaningful Factors of Hypoxic Fetal Injuries Herald of the National Technical University “KhPI”. Subject issue: Information Science and Modelling. – Kharkov: NTU “KhPI”, 2016.
  7. I. Skarga-Bandurova and T. Biloborodova, Data analysis for prognostication in cerebral status of newborns Herald of the National Technical University “KhPI”. Subject issue: Information Science and Modelling. – No. 33 (1142),   pages 134-143, 2015.
  8. I. Skarga-Bandurova, T. Biloborodova, and M. Nesterov, Modeling Structures for Integrated Obstetrics, Gynecology, and Neonatal Information System, Journal of Problems of Information Technologies. –   No. 17, pages 51-57, 2015.


Project started: 2018

Team Dev:

Tetiana Biloborodova, Data Analyst, PM

Oleksandr Berezhnyi, Product Engineer

Maksym Nesterov, DB Administrator, System Architect


Project website:

Control of motor activities is a key clinical need for patients with Parkinson’s disease. Such symptoms are difficult to assess in the outpatient department, and their assessment is often based on unreliable quizzes and questionnaires.

The goal is to develop an affordable and easy-to-use solution for remote monitoring of symptoms of Parkinson’s disease at home, a kind of electronic patient diary for a continuous long-term assessment of the motor conditions. The present work is in line with the efforts toward predictive healthcare data analytics, in capturing early signs of PD as well as assessing the development of Parkinsonian symptoms over time.  The idea of ​​this approach involves a combination of well-known methods for testing patients with PD, namely specially designed tests built-in as an application in a smartphone and enable to perform analysis of data in a different time and population scales.

The MeCo system includes the following components:

1) A smartphone app for iOS and Android that enables patients to carry out different motor performance tests complete a self-assessment questionnaire and securely submit data to the cloud service.

2) A web application to collect data from wearable sensors.

3) Scalable cloud-based data collection services that collect data from patients’ smartphones; it also ensures secure data management, and applies the data processing pipeline (see Fig. 2).

4) A webserver to host the site and store the signals.

5) Data-mining package for health data analytics incorporating quantitative and semi-structured data, and longitudinal analyses, clustering and classification; and a clinical user interface including short term and long term data visualization.

The basic structure of our functional architecture is shown in Fig. 2. Raw data from all sensors are processed and then used to perceive the current state. This process is mainly composed of three steps: collection, processing and analysis. These steps are explained below.

The MeCo mobile application is created for control of motor fluctuations, their dependence on time, a mode of day, a physical and psychological condition, type and intensity of medical treatment.


Smartphone-Based System for Long-Term Monitoring of Patients with Parkinson’s Disease

  1. Biloborodova T., Skarga-Bandurova I., Derevyanchenko V., Skarha-Bandurov I., Tatarchenko H. Multimodal Data Analysis in Personal Mobile System of Parkinson’s Disease. Special Issue on Convergence of Cloud, IoT and Big Data: New Platforms and Applications, 2021 – in press.
  2. Biloborodova T., Skarga-Bandurova I., Berezhnyi O., Nesterov M., Skarha-Bandurov I. (2020) Multimodal Smartphone-Based System for Long-Term Monitoring of Patients with Parkinson’s Disease. In: Rocha Á., Ferrás C., Montenegro Marin C., Medina García V. (eds) Information Technology and Systems. ICITS 2020. Advances in Intelligent Systems and Computing, vol 1137. Springer, Cham.
  1. Biloborodova T., Skarga-Bandurova I., Derevyanchenko V., Skarha-Bandurov I., Tatarchenko H., Mokhonko V. A Personal Mobile Sensing System for Motor Symptoms Assessment of Parkinson’s Disease. 2019 7th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW). – IEEE, 2019. – pp. 146-151.


Wearable and Embedded IoT-based applications  

  1. I. Kotsiuba, M. Nesterov, Yu.Yanovich, I. Skarga-Bandurova, T. Biloborodova, V.  Zhygulin. MultiDatabase Monitoring Tool for the EHealth Services. 2018 IEEE International Conference on Big Data, December 10-13, 2018, Seattle, WA, USA, pp. 2442-2448.
  2. Inna Skarga-Bandurova, Tetiana Biloborodova, Wearable and Embedded IoT-based Solutions for Biomedical Applications in Internet of Things for Industry and Human Applications.Volume 3. Assessment and Implementation. IoT for Healthcare systems. Section 46. / Ed. V. S. Kharchenko. – Ministry of Education and Science of Ukraine, National Aerospace University KhAI, 2018, pp. 535-575.
  3. Inna Skarga-Bandurova, Tetiana Biloborodova, Devices with Reconfigurable Architecture for Biomedical IoT-based Applications in Internet of Things for Industry and Human Applications. Volume 3. Assessment and Implementation. IoT for Healthcare systems.Section 47./ Ed. V. S. Kharchenko. – Ministry of Education and Science of Ukraine, National Aerospace University KhAI,2018, pp. 576-596.

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