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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Item type:Publication, CRITICAL OXYGEN SATURATION-LEVEL ESTIMATION FROM PHOTOPLETHYSMOGRAM (PPG): A PRISMA-COMPLIANT SYSTEMATIC REVIEW AND META-ANALYSIS(World Scientific, 2024-10-04); ; ; Sidorenko, MarijaObjectives: Photoplethysmogram (PPG) signals have become a crucial tool in the non-invasive monitoring of oxygen saturation levels (SpO2). The main purpose of the present review is to perform a meta-analysis of the involvement and consideration of critical SpO2 levels (<90%) in the research papers where SpO2 levels are calculated/predicted from PPG and to elaborate on the impact of the critical levels when presenting the evaluation results. Data sources: PubMed, Science Direct, and Scopus were searched for papers published between January 1, 2016, and September 10, 2022. Results: This study produced several results, concerning the main objective as well as other important issues for improving the SpO2 estimation/calculation. We discovered that only 21 out of 75 papers considered SpO2 values that are in the critical domain. Many papers do not provide access to their databases or disclose the software/models used. Additionally, some studies lack sufficient testing subjects and fail to make their results reproducible. The findings reveal a preference for SpO2 calculation over prediction, limited data availability, undisclosed methodologies, and diverse evaluation metrics hinder replication and direct comparisons between studies. Also, a scoring table is offered that scores higher the papers that are more valuable for SpO2 calculation/prediction.Conclusion: Employing PRISMA guidelines, a comprehensive search across PubMed, Science Direct, and Scopus databases initially extracted 6173 potential papers. Following rigorous screening, 75 papers were selected for detailed analysis, of which only 21 included data from critical SpO2 levels. Furthermore, this research provided information for the filtered 21 paper about the sample size of the study participants, the models utilized to derive the results, the availability of databases, the specific devices employed in the research, the methodologies employed for PPG signal measurement, and the collaborative efforts among authors from different institutions. This information is sublimed in the scoring table which gives higher scoring to those papers that are more valuable for SpO2 calculation/prediction. This study offers references to all these findings that can be used as concrete guidelines for prospective researchers and developers of new sensors for SpO2 estimation/calculation utilizing PPG signals. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Processing MIMIC-III for Evaluation of Various Blood Pressure Estimation Models(2023-10); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Processing MIMIC-III for Evaluation of Various Blood Pressure Estimation Models(2024); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Laser-Induced Graphene for Heartbeat Monitoring with HeartPy Analysis(MDPI, 2022-08-23); Madevska Bogdanova, AnaThe 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Wearable Patch for Mass Casualty Screening with Graphene Sensors(2022); Madevska Bogdanova, AnaWearable 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Estimation of Respiratory Rate from ECG signal in Python programming language(2022-09) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Using Cuffless Non-Invasive Methods for Blood Pressure Estimation: Description of the Selected Solutions(2022-09) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Blood pressure classification using CNN-LSTM model(2022-09); Blood pressure (BP) estimation can aid the triage process and help prioritizing and helping injured, especially in a situation of multiple casualties. The presented research aims to create a model for BP class estimation using electrocardiogram (ECG) and photoplethysmogram (PPG) waveforms. We focus on developing a BP classification model as a convolutional neural network (CNN) - gated recurrent unit (LSTM) hybrid model, containing both CNN and LSTM layers. The used dataset is the publicly available UCI Machine Learning Repository dataset. We have achieved stable AUCROC for each class - 0.89, 0.83, and 0.89 respectively and overall accuracy of 83%. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Evaluation of Python HeartPy Tooklit for Heart Rate extraction from PPG(2021); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Wearable Patch for Mass Casualty Screening with Graphene Sensors(2022); Madevska Bogdanova, AnaWearable 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.