Перегляд за Автор "Kovalchuk, Oleksii"

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    Документ
    ECG Arrhythmia Classification and Interpretation using Convolutional Networks for Intelligent IoT Healthcare System
    (CEUR-WS.org, 2024-07-29) Kovalchuk, Oleksii; Barmak, Olexander; Radiuk, Pavlo; Krak, Iurii
    In modern healthcare, timely and precise diagnosis of arrhythmias can significantly impact patient outcomes, as arrhythmias are indicative of various cardiac disorders that require immediate attention. The classification of these irregular heartbeats based on electrocardiogram (ECG) signals is essential for the development of intelligent healthcare systems that can provide real-time monitoring and diagnosis, integrating seamlessly into smart city infrastructures and IoT-enabled smart homes. In this paper, we propose novel methods to enhance the classification and interpretation of arrhythmia by ECG signals based on convolutional neural network (CNN). Leveraging the MIT-BIH Arrhythmia Database, which includes 48 recordings from 47 patients, the proposed approach involved preprocessing the ECG signals into fragments and enhancing the CNN architecture with Batch Normalization layers and an additional convolutional layer. The network was trained and validated using statistical metrics namely accuracy, precision, recall, and F1-scores. The results demonstrated an overall classification accuracy of 99.43%, with particularly high precision and recall for Normal beats, Right bundle branch block beats, and Left bundle branch block beats, achieving F1-scores close to 100%. The introduced CNN showed superior performance in distinguishing between nine types of arrhythmias. However, the study highlighted the limitation of relying on clinical features for decision justification, especially in cases of overlapping pathologies. Overall, the findings suggest that the proposed approach can serve as a reliable supporting tool for arrhythmia diagnosis, offering high accuracy and potential integration into real-time monitoring systems.
  • Ескіз
    Документ
    Human-in-the-Loop Approach Based on MRI and ECG for Healthcare Diagnosis
    (CEUR-WS, 2022-12-14) Radiuk, Pavlo; Kovalchuk, Oleksii; Slobodzian, Vitalii; Manziuk, Eduard; Barmak, Oleksander; Krak, Iurii
    The presented study investigates a human-centric approach to implementing human-in-the-loop models for healthcare diagnostics. The following tasks were considered and addressed in this work: a) identify the features necessary for future healthcare diagnosis based on electrocardiogram signals in the human-in-the-loop model: P, T-peaks, QRS-complex, PQ and ST segments, and b) detect inflammatory processes in the heart muscle (myocardium) based on cardiac magnetic resonance imaging. As a result of our investigation, a novel approach was proposed for embedding (integrating) clinical knowledge about the nature of these phenomena into the electrocardiogram signal and magnetic resonance imaging. Domain knowledge about the sample’s nature is encoded similarly to the input information. Moreover, the convolution operation within our approach serves as an embedding mechanism. The results presented in the article are a starting point for using the models obtained by the proposed approach (human-in-the-loop models) for classification problems using deep learning and convolutional neural networks. Also, visual analysis shows the proposed approaches’ ability to solve practical clinical problems. It also ensures transparent interpretation of the obtained results as the human-in-the-loop model, which, in turn, is built according to the human-centric approach. Overall, our contribution allows the implementation of a scheme for obtaining artificial intelligence solutions based on the principles of trust in them.
  • Ескіз
    Документ
    Robust R-peak Detection using Deep Learning based on Integrating Domain Knowledge
    (CEUR.org, 2023-01-07) Kovalchuk, Oleksii; Radiuk, Pavlo; Barmak, Olexander; Krak, Iurii
    Electrocardiography (ECG) is a pivotal clinical technique for assessing heart function by recording its electrical activity. However, accurate processing and analysis of ECG signals, particularly the detection of R-peaks, remains challenging. Any inaccuracies in R-peak detection can significantly impact subsequent stages of analysis, potentially leading to incorrect diagnoses and treatment decisions. Therefore, in this study, we aim to refine the approach to identifying R-peaks in ECG signals by integrating knowledge of a reference ECG signal into the input signal, addressing the critical need for accurate R-peak detection in diagnosing various cardiac pathologies. The authors propose a novel method involving the integration of knowledge into the ECG signal, processing this information using a convolutional neural network, and post-processing the CNN model's results to identify R-peaks. The method was evaluated using various four well-known ECG databases. Comparative results, with an error margin of +-25 ms, revealed that the proposed approach was the top performer across almost all metrics and databases, frequently achieving accuracy scores of 0.9999 and demonstrating high precision, recall, and F1-score. Based on the investigation findings, the proposed approach is robust and reliable, with the best performance achieved on the QT database test set, offering a balanced and dependable solution for R-peak detection in ECG signals.