Faculty of Computer Science and Engineering

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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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Start date: 2022Subject: search.filters.subject.ECG

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Now showing 1 - 9 of 9
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    Detection of Premature Heartbeats
    (IEEE, 2022-05-23)
    Premature heartbeats are those that appear earlier than the regular ones due to contractions not originating from the Sinus Atrial Node, out of the normal heart rhythm. Although one might think this is a trivial task to detect, the distribution of premature heartbeats in the benchmark electrocardiograms shows it is not the case. We aim at finding the optimal method to detect premature heartbeats, threshold value and context level for temporal based approach. Methodology: Several methods are specified to calculate the relation of the premature heartbeat to a set of several previous instances and conduct experiments to present which averaging method (arithmetic, geometric and harmonic mean) delivers the best solution. Then, we calculate the deviation ratio to the average of these beats that affect the prematurity condition. Particularly, the goal is to find an average AV G of previous k beat-to-beat intervals RR, such that the ratio between the difference dRR of the analyzed RR and AV G versus AV G is more than a specific threshold T hr. Data: The comprehensive MIT-BIH Arrhythmia Electrocardiogram benchmark database is used in our evaluation. The analysis is conducted on the array of beat-to-beat intervals for the heartbeats preceding the premature one. Conclusion: The results show that the optimal method is based on the arithmetic mean AM We found that the larger k, the better performance is achieved. A sufficient performance with F1 score over 85% is achieved for k = 5 and T hr = 15%.
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    Detecting Ventricular Beats with Machine Learning Models
    (IEEE, 2022-05-23)
    Tudjarski, Stojancho
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    Stankovski, Aleksandar
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    This paper aims at modeling a classifier of Ventricular heartbeats by experimenting with the most advanced classic binary classifiers in different scenarios for feature engineering. Methodology: The results were acquired based on experimenting with XGBoost and Random Forest algorithms, as two of the most advanced classifiers not based on neural networks. Although the annotated ECG data sets contain records with several heartbeat classes, we focus on a model that would distinguish V from others (Non-V heartbeats). Considering that we are dealing with a highly imbalanced data set, we applied the SMOTE algorithm for data enrichment to provide a better-balanced data set for training the model. To acquire better results, we added new calculated features, with and without feature selection. For feature selection, we used the Fisher Selector algorithm. Data: We used MIT-BIH Arrhythmia benchmark database, with train/test split according to the patient-oriented splitting approach that separates the original dataset into two subsets with approximately equal sizes and distribution of heartbeat types. Conclusion: The best results are achieved with XGBoost algorithm with original feature set. We achieved precision of 91.36%, recall of 88.31% and F1 score of 89.81%. Results showed that oversampling does not provide significantly better overall model performance. Still, we would recommend this approach since in practice, when dealing with imbalanced data sets, this leads to more robust models that perform better with data outside the training and test sets, such as when the model is used in production.
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    From Continuous ECG Signals to Extracted Features for Machine Learning Models and Arrhythmia Annotations
    (IEEE, 2022-11-15)
    Tudjarski, Stojancho
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    Stankovski, Aleksandar
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    This paper describes the process of transforming an ECG signal as a continuous stream of numbers representing measured electrical voltages between the ECG electrodes into an output indicating the existence of arrhythmia. The ECG data stream is a structured array of converted analog signal values to digital data. Although this stream uses a continuous structure of numbers within a given range that depends on the bit resolution during conversion, it is still unstructured as a representation of the appearance of arrhythmia since it does not contain information about detected arrhythmia. This paper presents how to process ECG data, detect heartbeat annotations, and calculate various parameters for tabular-based data with a fixed number of columns to be used as input into ML-based algorithms. Our use case addresses an ML algorithm to detect atrial fibrillation arrhythmia, as an irregular heart rhythm. Practically a set of numbers in the ECG samples, which do not have structured arrhythmia annotations, is transformed into structured annotations. Experiments are conducted on the well-known ECG benchmark MIT-BIH Arrhythmia Database. The input data is resampled from 360 Hz to 125 Hz signal, and a signal processing algorithm is used to detect heartbeats, extracting a fixed set of features, and systematically forwarded to the feature selection ML methodologies to obtain atrial fibrillation annotations.
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    Sampling Rate Impact on Heart Rate Variability
    (IEEE, 2022-11-15)
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    Tudjarski, Stojancho
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    Anagelevska, Ana
    We set a research question to find the impact of the sampling rate in the electrocardiograms on the calculation of the heart rate variability. Our evaluation method analyzes the heart rate variability on the MITDB electrocardiogram benchmark database originally sampled to 360 Hz. Then the electrocardiogram records are resampled to 125 Hz, and a new set of heart rate variability parameters are calculated and compared to those calculated on 360 Hz. The analysis performed on time-domain heart rate variability parameters showed that the impact of sampling frequency was large for NN50 and pNN50 parameters (up to a max value of 50%, and mean of 10%), and smaller for SDNN and RMSSD (up to a max value of 10%, and an average of 2%). In conclusion, heart rate variability needs a higher sampling frequency to reveal more precise results.
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    Effect of Bézier Interpolation on Similarity of Heartbeats in Electrocardiograms
    (IEEE, 2022-11-15)
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    Tonkovikj, Lucija
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    Petrovski, Nikola
    Sampling frequency and bit resolution impact the presentation of heartbeats in electrocardiograms. One of the most challenging tasks in automated medical interpretation is the determination of the heartbeat type, whether the beat was initiated by a normal sequence, or by the ventricle. The usual way to detect the beat class is to analyze the morphological features including shape, width and height of the heartbeat. In this paper, we tackle this problem by checking the similarity of the heartbeats and set a research hypothesis that Bézier interpolation can improve the correctness of the similarity check. We found that the similarity check of two heartbeats can be improved a lot by using Bézier interpolation on the samples around the detected peak.
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    Improving Atrial Fibrillation Detection with Machine Learning Models
    (IEEE, 2025-06-02)
    Tudjarski, Stojancho
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    Madevska Bogdanova, Ana
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    Stankovski, Aleksandar
    In this paper, we focus on feature engineering to detect Atrial Fibrillation by determining whether the heart rhythm has irregularities without patterns. We experiment with a broad spectrum of features derived from the duration of heartbeat-to-heartbeat intervals in the benchmark electrocardiogram databases MIT-BIH Arrhythmia Database, MIT-BIH Atrial Fibrillation Database, and Long Term AF Database. The experiments included position-based features, fluctuation indices, standard deviations, mean average values, Shannon entropy, and statistical measures, such as compressed time series data length. The research questions are to detect the most influential features that result in the best-performing model and the impact of the training dataset. Our approach is to evaluate the model on a completely different dataset from the one it was trained on. Achieved F1 scores vary between 62,32% and 85.08%. The results prove that positional features increase the model's performance.
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    Scalability Evaluation of HPC Multi-GPU Training for ECG-based LLMs
    (IEEE, 2025-06-02)
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    Petrovski, Nikola
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    Training large language models requires extensive processing, made possible by many high-performance computing resources. This study compares multi-node and multi-GPU environments for training large language models using electrocardiogram data. It provides a detailed mapping of current frameworks for distributed deep learning in multi-node and multi-GPU settings, including Horovod from Uber, DeepSpeed from Microsoft, and the built-in distributed capabilities of PyTorch and TensorFlow. We compare various multi-GPU setups for different dataset configurations, utilizing multiple HPC nodes independently and focusing on scalability, speedup, efficiency, and overhead. The analysis leverages HPC infrastructure with SLURM, Apptainer (Singularity) containers, CUDA, PyTorch, and shell scripts to support training workflows and automation. We achieved a sub-linear speedup when scaling the number of GPUs, with values of 1.6x for two GPUs and 1.9x for four GPUs.
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    Overview of Interpretable and Explainable Artificial Intelligence for Atrial Fibrillation
    (IEEE, 2025-11-25)
    Tudjarski, Stojancho
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    Angjelevska, Ana
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    Madevska Bogdanova, Ana
    Accurate 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.
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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.