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.

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Author: search.filters.author.Tomić, Miona

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    Prediction of Oxygen Saturation from Graphene Respiratory Signals with PPG Trained DNN
    (SCITEPRESS - Science and Technology Publications, 2024)
    ;
    Vićentić, Teodora
    ;
    Madevska Bogdanova, Ana
    ;
    Ilić, Stefan
    ;
    Tomić, Miona
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    Prediction of Oxygen Saturation from Graphene Respiratory Signals with PPG Trained DNN
    (SCITEPRESS - Science and Technology Publications, 2024)
    ;
    Vićentić, Teodora
    ;
    Madevska Bogdanova, Ana
    ;
    Ilić, Stefan
    ;
    Tomić, Miona
    This paper explores the feasibility of using wearable laser-induced graphene (LIG) sensors to estimate oxygen saturation (SpO2) as an alternative to traditional photoplethysmography (PPG) oximeters, particularly in mass casualty triage scenarios. Positioned on the chest, the LIG sensor continuously monitors respiratory signals in real-time. The study leverages deep neural network (DNN) trained on PPG signals to process LIG respiratory signals, revealing promising results. Key performance metrics include a mean squared error (MSE) of 0.152, a mean absolute error (MAE) of 1.13, a root mean square error (RMSE) of 1.23, and an R2 score of 0.68. This innovative approach, combining PPG and respiratory signals from graphene, offers a potential solution for 2D sensors in emergency situations, enhancing the monitoring and management of various medical conditions. However, further investigation is required to establish the clinical applications and correlations between these signals. This study marks a significant step toward advancing wearable sensor technology for critical health- care scenarios.
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    Blood Oxygen Saturation Estimation with Laser-Induced Graphene Respiration Sensor
    (Hindawi Limited, 2024-01-29)
    Madevska Bogdanova, Ana
    ;
    ;
    Vićentić, Teodora
    ;
    D. Ilić, Stefan
    ;
    Tomić, Miona
    Measuring blood oxygen saturation (SpO2) is crucial in a triage process for identifying patients with respiratory distress or shock, since low SpO2 levels indicate compromised hemostability and the need for priority treatment. This paper explores the use of wearable mechanical deflection sensors based on laser-induced graphene (LIG) for SpO2 estimation. The LIG sensors are attached to a subject’s chest for real-time monitoring of respiratory signals. We have developed a novel database of the respiratory signals, with corresponding SpO2 values ranging from 86% to 100%. The database is used to develop an artificial neural network model for SpO2 estimation. The neural network performance is promising, with regression metrics mean squared error = 0.184, mean absolute error = 0.301, root mean squared error = 0.429, and R-squared = 0.804. The use of mechanical respiration sensors in combination with neural networks in biosensing opens new possibilities for noninvasive SpO2 monitoring and other innovative applications.