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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    Trends from Minimally Invasive to Non-invasive Glucose Measurements
    (IEEE, 2020-09-28)
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    Poposka, L.
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    Guseva, E.
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    Approximately 7% of the elderly over 45 are diagnosed in several diabetes forms and it is believed that 3% of the population is undiagnosed. As the number of diabetes diagnoses has been increasing, so too has the technology for managing the disease. The traditional way of measuring glucose levels is to apply a blood drop to a chemically treated, disposable ”test-strip” and insert it into an electronic device. In this study, we overview the latest technology for continuous glucose measurement, as they trend from minimally invasive to non-invasive glucose measurement methods.
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    Efficient Time-Series Heart Rate Variability Metrics in C++
    (IEEE, 2025-06-02)
    Shekerov, A.
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    Angjelevska, A.
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    Time-Series Heart Rate Variability is an essential set of metrics in modern medicine and cardiology. Algorithms for HRV calculations exhibit quadratic time complexity in the worst cases, as metrics are calculated at many pre-defined time intervals with different widths and offsets, especially for patients with potentially week-long recordings. In this paper, we aim to develop an efficient software solution for electrocardiograms from wearable sensors, which requires careful preprocessing and filtering steps to eliminate noise and erroneous values, detect heartbeats, and classify arrhythmia. This paper describes the utilized method and presents an efficient C++ implementation. We evaluate efficiency by comparing the new solution with the existing Python implementation and conclude that the new implementation performs exceptionally better, with a median speedup of 81.4 for single-day patient recordings and an average speedup of 11.9 for multi-day patient recordings. The results are presented for an extensive set of preprocessed patient databases, discussing the benefits and drawbacks.
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    Processing MIMIC-III for Evaluation of Various Blood Pressure Estimation Models
    (2024)
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    Kuzmanov, Ivan
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    Lehocki, Fedor
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    Madevska Bogdanova, Ana
    The development of non-invasive easily available blood pres- sure estimation methods using electrocardiogram - ECG and/or photo- plethysmogram - PPG signals has gained increasing attention. However, there is a lack of consistency in the evaluation of these methods due to variations in the size and availability of data in published datasets. Our research involves retrieving, cleaning, and storing a portion of the MIMIC-III database for utilization in model training and testing. This paper outlines our methodology for processing the MIMIC-III database, along with the challenges encountered during the process.
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    Integrated Smart Patch for Heart Rate and Respiratory Rate Monitoring
    (IEEE, 2023-07)
    Daniel Gogola
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    Richard Bagín
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    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.