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Deep Learning Image Embeddings for Ventricular Beat Classification
(IEEE, 2025-11-25)
Tudjarski, Stojancho
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Petrovski, Nikola
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Ventricular arrhythmias pose significant risks to cardiovascular health, necessitating accurate detection from electrocardiogram signals. This study investigates the effectiveness of vector embeddings derived from various convolutional neural networks and contrastive language-image pretraining-based models for classifying ventricular heartbeats presented as images. A comparative analysis revealed that the combination of the ResNet50 model and support vector machines achieved the top F1 score of 79.30%. The results provide insights into how deep neural network models can produce effective vector embeddings of heartbeats to train heartbeat classification models.
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Optimizing Heartbeat Classification using Bézier Interpolation
(IEEE, 2023-05-22)
Gusev, Marjan
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Petrovski, Nikola
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Tonkovikj, Lucija
Analog to digital conversion of electrocardio-grams depends on the sampling frequency influencing the determination of a proper heartbeat location and precision of further digital processing. We set a research question to find the optimal number of interpolation points to reduce the mistakes in the similarity check of heartbeats and classify ventricular beats. In addition, another research question aims at finding the optimal number of interpolation points applying Bézier interpolation to reveal the optimal performance/cost ratio. Final research question is to find the sampling frequency that will reveal optimal performance in classification of ventricular beats. The experiments evaluate all neighbouring pairs of heartbeats from the standard benchmark MIT-BIH arrhythmia dataset resampled to a 125 Hz sampling frequency. The results show that even one more interpolation point, which corresponds to a sampling frequency of 250 Hz, will increase the performance versus the original 360 Hz sampling frequency. At the same time, the optimal is interpolation with additional five or seven points corresponding to 750 Hz, and 1000 Hz respectively. We found that a threshold value of 34 reveals the optimal performance to conclude a change between ventricular heartbeats and others, even in a 10-bit precision of the analog-digital conversion. The processing time and performance/cost-benefit analysis show that one interpolation point is the most beneficial.
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1D Convolutional Neural Network for Atrial Fibrillation Detection
(IEEE, 2023-11-21)
Petrovski, Nikola
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Gusev, Marjan
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Tudzarski, Stojancho
Biomedical Informatics faces a paramount challenge: efficient and accurate Atrial Fibrillation detection from a single-lead electrocardiogram. A cutting-edge Convolutional Neural Network model is constructed on differences of consecutive heart rate samples and extracting datasets with specific arrhythmia in the training process, being unique in the feature engineering and model development.Our model resulted in an F1 Score of 98.14%, evaluated by 10-fold cross-validation using the label evaluation method, comparable to other best results. However, we have proven that label-based methods are substantially higher than those based on inter-patient training/testing data split with different datasets, such as those used as ECG benchmarks detecting AFIB and the corresponding evaluation methods based on duration instead labels, such as those used in standards evaluating ambulatory ECG monitoring devices. Evaluation of our model with the inter-patient data split and duration-based evaluation method showed a 94.16% F1 score, which complies with standards and is high above the standard recommendations. This affirms the high efficacy and potential of the developed solution.

Faculty of Computer Science and Engineering

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