Faculty of Computer Science and Engineering

Permanent URI for this communityhttps://repository.ukim.mk/handle/20.500.12188/5

The Faculty of Computer Science and Engineering (FCSE) within UKIM is the largest and most prestigious faculty in the field of computer science and technologies in Macedonia, and among the largest faculties in that field in the region. The FCSE teaching staff consists of 50 professors and 30 associates. These include many “best in field” personnel, such as the most referenced scientists in Macedonia and the most influential professors in the ICT industry in the Republic of Macedonia.

Browse

Settings

Author: search.filters.author.Lehocki, FedorHas files: No

Search Results

Now showing 1 - 8 of 8
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Scoping Review of Technology Enabled Healthcare Integration - Towards Sustainable Care
    (IEEE, 2025-06-16)
    Loncar-Turukalo, Tatjana
    ;
    ;
    Madevska Bogdanova, Ana
    ;
    Solarevic, Milica
    ;
    Lehocki, Fedor
    Technology serves as a relevant facilitator in integration of care, supporting healthcare across many pillars, such as diagnoses, drug-development, administration and coordination of services, but as well playing a pivotal role in disease prevention and pervasive health monitoring. This study presents the scoping review of the literature corpus related to technology trends supporting healthcare integration. The study explores 5 digital libraries from major publishers, aiming to identify changes in research trends in the decade from 2014 to 2024, and investigate which implementation challenges, barriers and risks present the major focus in healthcare integration. In total 14721 relevant studies were identified and included in collating and summarizing of the results in this work. The study shows that there is a triple increase in the number of studies from 2020 onwards, mainly focused on technology and implementation challenges. The main topic of the studies shifts from Internet of Medical Things (IoMT) until 2019 to AI in healthcare, which gained its momentum by continual advances in AI performance across different tasks. Enhanced health care, holistic care and patient experience are the most targeted benefits and opportunities of integrated care, while security and privacy of health related data remain the main concerns and playground for further research.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Integrated Smart Patch for Heart Rate and Respiratory Rate Monitoring
    (IEEE, 2023-05-29)
    Gogola, Daniel
    ;
    ;
    Bagín, Richard
    ;
    ;
    Madevska Bogdanova, Ana
    A wearable smart patch was designed to monitor the vital parameters of mass casualties’ victims after the first triage. The device captures ECG, PPG, and respiration signals and triggers an alarm if the heart rate (HR) or respiration rate (RR) exceeds the specified limits and indicates a threat to the victim's life. To obtain a robust and reliable solution, the same parameters are derived from two or three independent signals. In this study, ECG signals have been recorded from different positions on the chest, and the performance of several algorithms for HR and RR extraction was tested. The initial measurements show that HR estimation is more accurate and reliable than RR estimation. The best results, considering both, the HR and RR calculations, were achieved when Pan-Tompkins’s algorithm was used, and ECG electrodes were placed vertically on the right anterior chest. Increasing the length of the evaluated ECG signal above 30 seconds did not significantly affect the HR and RR calculation, regardless of the algorithm used.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    A Deep Learning Approach to Estimate SpO2 from PPG Signals
    (ACM, 2022-09-18)
    ;
    Madevska Bogdanova, Ana
    ;
    Mitrova, Hristina
    ;
    Sidorenko, Marija
    ;
    Lehocki, Fedor
    Blood oxygen saturation level (SpO2) is one of the vital parameters determining the hemostability of a patient, besides heart rate (HR), respiratory rate (RR) and blood preasure (BP). In emergency situations with a high number of injured persons, during the second triage until arrival to a medical facility, continuously following the SpO2 level in real time is of outmost importance. Using a smart patch-like device attached to a injured’s chest that contains a Photoplethysmogram (PPG) sensor, one can obtain the SpO2 parameter. Our interest in the process of the smart patch prototype development is to investigate the monitoring of a blood oxygen saturation level by using the embedded PPG sensor. We explore acquiring the SpO2 by extracting the set of features from the PPG signal utilizing Python toolkit HeartPy in order to model a Deep neural network regressor. The PPG signal is preprocessed by various filtering techniques to remove low/high frequency noise. The model was trained and tested using the clinical data collected from 52 subjects with SpO2 levels varying from 83 - 100%. The best experimental results considering the SpO2 interval [83,95) were achieved with a PPG signal of 10 seconds length (MAPE 2.00% and 7.21% of big errors defined as absolute percentage errors (APE) equal or greater than 5).
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Machine learning based SpO2 prediction from PPG signal's characteristics features
    (IEEE, 2022-06-22)
    ;
    Mitrova, Hristina
    ;
    Madevska Bogdanova, Ana
    ;
    Lehocki, Fedor
    Continuous monitoring of blood oxygen saturation level (SpO2) during the second triage in the high casualty event and determining the hemostability of a patient/victim until arrival to a medical facility, is essential in emergency situations. Using a SmartPatch device attached to a victim's chest that contains a Photoplethysmogram Waveforms (PPG) sensor, one can obtain the SpO2 parameter. Our interest in the process of the SmartPatch prototype development is to investigate the monitoring of a blood oxygen saturation level by using the embedded PPG sensor. We explore acquiring the Sp02 by extracting the set of features from the PPG signal utilizing two Python toolkits, HeartPy and Neurokit, in order to model the Machine learning predictors, using multiple regressors. The PPG signal is preprocessed by various filtering techniques to remove low/high frequency noise. The model was trained and tested using the clinical data collected from 52 subjects with SpO2 levels varying from 83 – 100%. The best experimental results - MAE (1.45), MSE (3.85), RMSE (1.96) and RMSLE (0.02) scores are achieved with the Random Forest regressor in the experiment with 7 features extracted from the both toolkits.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Laser-Induced Graphene for Heartbeat Monitoring with HeartPy Analysis
    (Sensors, 2022-08-23)
    Vićentić, Teodora
    ;
    Rašljić Rafajilović, Milena
    ;
    Ilić, Stefan
    ;
    ;
    Madevska Bogdanova, Ana
    The HeartPy Python toolkit for analysis of noisy signals from heart rate measurements is an excellent tool to use in conjunction with novel wearable sensors. Nevertheless, most of the work to date has focused on applying the toolkit to data measured with commercially available sensors. We demonstrate the application of the HeartPy functions to data obtained with a novel graphene-based heartbeat sensor. We produce the sensor by laser-inducing graphene on a flexible polyimide substrate. Both graphene on the polyimide substrate and graphene transferred onto a PDMS substrate show piezoresistive behavior that can be utilized to measure human heartbeat by registering median cubital vein motion during blood pumping. We process electrical resistance data from the graphene sensor using HeartPy and demonstrate extraction of several heartbeat parameters, in agreement with measurements taken with independent reference sensors. We compare the quality of the heartbeat signal from graphene on different substrates, demonstrating that in all cases the device yields results consistent with reference sensors. Our work is a first demonstration of successful application of HeartPy to analysis of data from a sensor in development.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    SmartPatch for Victims Management in Emergency Telemedicine
    (IEEE, 2021-05-17)
    Lehocki, Fedor
    ;
    Madevska Bogdanova, Ana
    ;
    Tysler, Milan
    ;
    Ondrusova, Beata
    ;
    Wearable real-time systems collecting and smartly analyzing information about patient health status could help medical personnel adopting the most suitable countermeasures in case of highly stressful situations in military and civil scenarios. Such situations include terrorist attacks or rescue operations. We propose the design and development of a patch-like device prototype (SmartPatch) and a methodology enabling continuous evaluation of victims' vital parameters. Using this innovative platform after the first triage, the onsite emergency teams will have continuous information about the health status of each person wearing the SmartPatch. If the health status of a victim is changed, SmartPatch is able to generate an alert and prevent overlook of critical health changes causing potential severe life-threatening consequences or death.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    What Clinics are Expecting From Data Scientists? A Review on Data Oriented Studies Through Qualitative and Quantitative Approaches
    (Institute of Electrical and Electronics Engineers (IEEE), 2019)
    Xu, Lina
    ;
    ;
    ;
    ;
    Madevska Bogdanova, Ana
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,