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.

Browse

Settings

Author: search.filters.author.Kuzmanov, IvanHas files: No

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    A study on appropriate segment length for generalized cuff-less blood pressure estimation from ECG features
    (IEEE, 2024-05-20)
    Kuzmanov, Ivan
    ;
    ;
    Lamenski, Petre
    ;
    ;
    Madevska Bogdanova, Ana
    Blood 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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    A study on appropriate segment length for generalized cuff-less blood pressure estimation from ECG features
    (IEEE, 2024-05-20)
    Kuzmanov, Ivan
    ;
    ;
    Lamenski, Petre
    ;
    ;
    Madevska Bogdanova, Ana
    Blood 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.