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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    Prediction of Oxygen Saturation from Graphene Respiratory Signals with PPG Trained DNN
    (SCITEPRESS - Science and Technology Publications, 2024)
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    Vićentić, Teodora
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    Madevska Bogdanova, Ana
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    Ilić, Stefan
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    Tomić, Miona
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    Prediction of Oxygen Saturation from Graphene Respiratory Signals with PPG Trained DNN
    (SCITEPRESS - Science and Technology Publications, 2024)
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    Vićentić, Teodora
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    Madevska Bogdanova, Ana
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    Ilić, Stefan
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    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
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    Vićentić, Teodora
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    D. Ilić, Stefan
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    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.
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    Laser-Induced Graphene for Heartbeat Monitoring with HeartPy Analysis
    (MDPI, 2022-08-23)
    Vićentić, Teodora
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    Rašljić Rafajilović, Milena
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    Ilić, Stefan
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    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.
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    Wearable Patch for Mass Casualty Screening with Graphene Sensors
    (2022)
    Vićentić, Teodora
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    Rašljić Rafajilović, Milena
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    Ilić, Stefan
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    Madevska Bogdanova, Ana
    Wearable sensors are reaching maturity, at the same time as technologies for communicating physiological data and those for analyzing massive amounts of data. The combination of the three technologies invites for applications in mass screening of personal health through smart algorithm deployment on data from wearable patches. We propose and present an architecture for a wearable patch to be used in mass casualty emergency situations, or for hospital bedside monitoring. The proposed patch will contain multiple sensors of physiological parameters. We propose to create respiration and heartbeat sensors made of laser induced graphene. We show that graphene on flexible substrates can be utilized in conjunction with the Python heart rate analysis toolkit - HeartPy to reliably acquire physiological data from human subjects.
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    Estimation of Respiratory Rate from ECG signal in Python programming language
    (2022-09)
    Žňava, Eduard
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    Lehocki, Fedor
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    Tyšler, Milan
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    Madevska Bogdanova, Ana
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    In case of mass casualties, it is necessary to obtain different vital signs including respiratory rate effectively and accurately. The more physiological signals are measured individually - the more time it takes to obtain multiple vital signs. In addition, a lot of technical equipment is needed. Because of that, it is effective to derive multiple vital signs from measurement of one single physiological signal. It is possible to derive respiratory rate from ECG signal. In this paper, we are constructing an appropriate solution based on different methods for extraction of respiratory rate from ECG signal using Python programming language together with suitable Python libraries for data processing. We managed to implement three methods and validate the accuracy of the calculations by Pearson’s and Spearman’s coefficients of correlation, as well as by root mean square error between of the RR calculated from derived and measured respiration signal. For the best method, we completed the algorithm reaching the coefficients of correlation equal to 0.703 and 0.700. The root mean square error is equal to 1.84 breaths per minute.
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    Wearable Patch for Mass Casualty Screening with Graphene Sensors
    (2022)
    Vićentić, Teodora
    ;
    Rašljić Rafajilović, Milena
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    Ilić, Stefan
    ;
    ;
    Madevska Bogdanova, Ana
    Wearable sensors are reaching maturity, at the same time as technologies for communicating physiological data and those for analyzing massive amounts of data. The combination of the three technologies invites for applications in mass screening of personal health through smart algorithm deployment on data from wearable patches. We propose and present an architecture for a wearable patch to be used in mass casualty emergency situations, or for hospital bedside monitoring. The proposed patch will contain multiple sensors of physiological parameters. We propose to create respiration and heartbeat sensors made of laser induced graphene. We show that graphene on flexible substrates can be utilized in conjunction with the Python heart rate analysis toolkit - HeartPy to reliably acquire physiological data from human subjects.
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    Wearable Graphene Sensor for Pulse Measurement
    (Institute of Technical Sciences of SASA, 2021)
    Vićentić, Teodora
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    Rašljić Rafajilović, Milena
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    Madevska Bogdanova, Ana
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    Pašti, Igor
    Graphene is a material that has been widely used in different types of sensors, due to its optimal electrical and mechanical properties. Recently discovered laser-induced graphene (LIG) has opened new perspectives for a versatile and durable physical sensing platform, capable of detecting various physical parameters. In this study, LIG was produced by CO2 laser irradiation of polyimide film. LIG-based sensor was used to measure human pulse. Fabricated LIG sensors were attached to a subject’s body. A temporal change in resistance was recorded for different sensor embodiments, including as-fabricated on polyimide tape and transferred into a PDMS matrix. The measured change in resistance is induced by bending of the sensor due to vein pulsing. The measured resistance time traces were analyzed with the Python HeartPy module to extract pulse information, demonstrating agreement with commercially available sensors.