Kostoska, Magdalena
Preferred name
Kostoska, Magdalena
Official Name
Kostoska, Magdalena
Main Affiliation
Email
magdalena.kostoska@finki.ukim.mk
63 results
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Item type:Publication, SmartPatch for Victims Management in Emergency Telemedicine(IEEE, 2021-05-17) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Trends from Minimally Invasive to Non-invasive Glucose Measurements(IEEE, 2020-09-28); ; ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, TRANSITION FROM WEB FORMS TO .NET MVC - EXPERIENCE IN INTERNET TECHNOLOGY COURSE(IATED, 2020-03); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Serverless Real-Time Data Analytics Platform for Edge Computing(Institute of Electrical and Electronics Engineers (IEEE), 2017) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Medical Real-Time Data Analytics System Design Aspects, Reference Architecture and Evaluation(Springer International Publishing, 2018); ; ; Madevska Bogdanova, Ana - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Integrated Smart Patch for Heart Rate and Respiratory Rate Monitoring(IEEE, 2023-07); ; 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, Interhost Orchestration Platform Architecture for Ultrascale Cloud Applications(Institute of Electrical and Electronics Engineers (IEEE), 2021-05-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparison of Different Methods for Estimation of Arterial Blood Pressure Using PPG Signals(Springer, Cham, 2024-06-25); ; ; The use of photoplethysmography (PPG) signals to predict the arterial blood pressure (ABP) waveform has gained popularity in recent years. Currently, there is a limited number of studies investigating this approach. This chapter elaborates a comparative analysis of two methodologies: a deep neural network approach and an encoder–decoder model for ABP waveform estimation with different window sizes expressed in seconds: 1s (175 signal points) and 4s (512 signal points). By estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP) scalars, this approach differs from conventional regression models that predict blood pressure through direct estimation; and it also enables another feature—evaluation of cardiovascular anomalies by analyzing the waveform patterns derived from the input PPG signal, which enables further medical analysis. The best obtained results are an R2 score of 0.76 for ABP, an MAE of 6.52 mmHg for DBP, using an encoder–decoder model on a sequence of 4s, and an MAE of 10.48 mmHg for SBP using GRU neural network on a sequence of 1s. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Information System for Biosensors Data Exchange in Healthcare(Springer International Publishing, 2017-10-12); ; ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, LoCLoP: Low-cost/Low-processing Power Methodology for Deriving Heart Rate and Respiratory Rate in Time-critical Domain(IEEE, 2019-07); ; ;