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, Improving Atrial Fibrillation Detection with Machine Learning Models(IEEE, 2025-06-02); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Overview of Interpretable and Explainable Artificial Intelligence for Atrial Fibrillation(IEEE, 2025-11-25); Madevska Bogdanova, AnaAccurate and interpretable detection of irregular work of the heart, such as atrial fibrillation, from electrocardiogram (ECG) signals is crucial for timely diagnosis and effective patient management. While machine learning (ML) models, particularly deep learning architectures, have achieved state-of-the-art performance in ECG arrhythmia classification, their black-box nature limits clinical adoption. This paper explores explainable artificial intelligence (XAI) techniques applicable to ML models trained on ECG data, highlighting both global and local interpretability approaches. We provide an overview of posthoc methods, including SHAP, LIME, PFI, and LIG, among others, treating various types of ECG recordings. As a practical case study, we present our findings analyzing the results of PFI and LIG methods applied to a transformer-based model fine-tuned for atrial fibrillation detection and explain its decision process. Our findings underscore the value of integrating XAI into ECG analysis pipelines to enhance transparency, foster clinician trust, and support more informed decision-making in cardiovascular diagnostics. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Scoping Review of Technology Enabled Healthcare Integration - Towards Sustainable Care(IEEE, 2025-06-16); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Integrated Smart Patch for Heart Rate and Respiratory Rate Monitoring(IEEE, 2023-05-29); ; Madevska Bogdanova, AnaA 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Prediction of Oxygen Saturation from Graphene Respiratory Signals with PPG Trained DNN(SCITEPRESS - Science and Technology Publications, 2024); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Overview of Legal Artificial Intelligence Assistants Landscape(IEEE, 2025-11-25); ; ; This survey presents the current landscape of AI legal tools, serving both legal professionals and the general public. It compares existing solutions, while also addressing technological and business challenges that shape their development and use. The findings contribute to a clearer understanding of the role and potential of AI assistants in the legal domain, offering insights relevant to both practitioners and researchers. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Enhancing Privacy of Clinical Decision Support Systems with Federated Learning(IEEE, 2024-06-25) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A study on appropriate segment length for generalized cuff-less blood pressure estimation from ECG features(IEEE, 2024-05-20); ; ; Madevska Bogdanova, AnaBlood pressure (BP) refers to the pressure exerted on the blood vessels as blood travels through the body. Our ultimate goal is to build a stable model for BP estimation as part of a triage process. In this study, we experiment to determine a suitable signal segment only from electrocardiogram (ECG) signals, to ensure a fast and reliable process of the BP estimation. The used dataset contains only high-quality ECG and arterial blood pressure (ABP) signals extracted from the Medical Information Mart for Intensive Care, MIMIC II and MIMIC III databases by our methodology. It was processed three times using similar machine learning (ML) methodologies, with different segment lengths. Three different datasets are generated using a non-overlapping window with a size of 8, 15, and 30 seconds, with the same ECG features. Several linear and nonlinear Machine Learning models are built on these datasets, and their results are compared. Our best results were obtained by a light gradient-boosting machine (LightGBM) regression model trained on the 30-second dataset. The model achieves Mean Absolute Error (MAE) of 10.87, 6.55, and 7.29, and Root Mean Squared Error (RMSE) of 14.49, 8.68, and 9.68 for systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP), respectively. The results of our experiment indicate that a duration of 30 seconds is the minimum length that provides informative features, fulfilling the need for real-time delivery. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Game-based learning approach in computer science in primary education: A systematic review(Elsevier, 2024-01) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Blood Oxygen Saturation Estimation Using PPG Signals from the MIMIC-III Database(Springer, Cham, 2025-04-23); Madevska Bogdanova, AnaPhotoplethysmogram (PPG) signals are pivotal in cardiovascular monitoring, offering real-time insights into heart rate and oxygen saturation (SpO2). This study explores the creation of deep learning and machine learning models - specifically Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (BiLSTM) networks, Recurrent Neural Networks (RNNs), and Random Forest Regressors (RFRs)-to estimate SpO2 levels from single-channel PPG data. Another point is developing algorithms for using the data sourced from the PhysioNet MIMIC-III database. The patients used for training and testing are distinct, ensuring no overlap between the datasets and enabling rigorous model evaluation. A comprehensive analyses reveal that LSTM-based model achieve significant accuracy in SpO2 estimation, with R-squared value reaching up to 0.59. Specifically, the LSTM model demonstrated an MAE of 1.26, MSE of 3.11 and RMSE of 1.76. These results demonstrate the potential of machine learning techniques in advancing clinical monitoring and decision-making processes within critical care environments, thereby enhancing patient care outcomes.