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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    Estimation of Blood Pressure from Arterial Blood Pressure using PPG Signals
    (Faculty of Computer Science and Engineering, Skopje, North Macedonia, 2023-08)
    Mladenovska, Teodora
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    Madevska Bogdanova, Ana
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    Predicting Blood pressure from Photoplethysmography (PPG) signals is an active area of research and there have been many studies exploring the feasibility of this approach. This paper elaborates on a technique for the estimation of continuous Arterial blood pressure (ABP) waveform using PPG signals as inputs in a developed deep-learning model. The ultimate goal is estimating the Blood pressure, but unlike the standard regression models for predicting Blood pressure by systolic BP (SBP) and Diastolic BP (DBP), this approach calculates SBP and DBP from the estimated ABP waveform, which enables further analysis to enhance the BP estimation. The best-obtained results are an MAE of 8.40mmHg, and an MAE of 11.1mmHg and 7mmHg for SBP and DBP respectively. The promising prediction of SBP and DBP using our proposed machine learning model has the potential to improve clinical decision-making and resource allocation process in emergency situations.
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    A Deep Learning Approach to Estimate SpO2 from PPG Signals
    (ACM, 2022-09-18)
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    Madevska Bogdanova, Ana
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    Mitrova, Hristina
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    Sidorenko, Marija
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    Lehocki, Fedor
    Blood oxygen saturation level (SpO2) is one of the vital parameters determining the hemostability of a patient, besides heart rate (HR), respiratory rate (RR) and blood preasure (BP). In emergency situations with a high number of injured persons, during the second triage until arrival to a medical facility, continuously following the SpO2 level in real time is of outmost importance. Using a smart patch-like device attached to a injured’s chest that contains a Photoplethysmogram (PPG) sensor, one can obtain the SpO2 parameter. Our interest in the process of the smart patch prototype development is to investigate the monitoring of a blood oxygen saturation level by using the embedded PPG sensor. We explore acquiring the SpO2 by extracting the set of features from the PPG signal utilizing Python toolkit HeartPy in order to model a Deep neural network regressor. The PPG signal is preprocessed by various filtering techniques to remove low/high frequency noise. The model was trained and tested using the clinical data collected from 52 subjects with SpO2 levels varying from 83 - 100%. The best experimental results considering the SpO2 interval [83,95) were achieved with a PPG signal of 10 seconds length (MAPE 2.00% and 7.21% of big errors defined as absolute percentage errors (APE) equal or greater than 5).