Robust R-peak Detection using Deep Learning based on Integrating Domain Knowledge
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Дата
2023-01-07
Автори
Kovalchuk, Oleksii
Radiuk, Pavlo
Barmak, Olexander
Krak, Iurii
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Номер ISSN
Назва тому
Видавець
CEUR.org
Анотація
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.
Опис
Ключові слова
Healthcare diagnosis, electrocardiogram, ECG monitoring, R-peak detection, domain knowledge, deep learning
Бібліографічний опис
Kovalchuk O., Radiuk P., Barmak O., Krak Iu. Robust R-peak detection using deep learning based on integrating domain knowledge. The 6th International Conference on Informatics & Data-Driven Medicine (IDDM-2023) : CEUR-Workshop Proceedings. Vol. 3609. (Bratislava, Slovakia, 17-19 November 2023). CEUR-WS.org, Aachen, 2023. P. 1-14. URL: https://ceur-ws.org/Vol-3609/paper1.pdf