Benchmarking ECG Delineation using Deep Neural Network-based Semantic Segmentation Models

Jaeho Park, TaeJun Park, Joon-myoung Kwon, Yong-Yeon Jo
Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:63-88, 2025.

Abstract

Accurate electrocardiogram (ECG) delineation is essential for automated cardiac diagnosis, enabling the precise identification of key waveforms such as the P wave, QRS complex, and T wave. This study presents the first comprehensive benchmarking of neural network-based semantic segmentation models for ECG delineation, evaluating their accuracy, resource efficiency, and robustness across both public and private datasets. Our results demonstrate that convolutional neural network (CNN)-based approaches consistently achieve superior accuracy compared to other network architectures. Additionally, we observed the presence of fragmented segments in the delineation results. To address this issue, we explored post-processing techniques to consolidate or eliminate fragmented segments using an optimal configuration, leading to performance improvements. Furthermore, by analyzing performance variations across different waveform labels, we provide critical insights into key considerations for ECG segmentation tasks. Notably, our findings also reveal that larger model sizes do not necessarily correlate with better performance. Based on our findings, we propose a set of practical guidelines for leveraging segmentation models in ECG delineation, offering valuable direction for future research and clinical applications.

Cite this Paper

BibTeX
@InProceedings{pmlr-v287-park25a, title = {Benchmarking ECG Delineation using Deep Neural Network-based Semantic Segmentation Models}, author = {Park, Jaeho and Park, TaeJun and Kwon, Joon-myoung and Jo, Yong-Yeon}, booktitle = {Proceedings of the sixth Conference on Health, Inference, and Learning}, pages = {63--88}, year = {2025}, editor = {Xu, Xuhai Orson and Choi, Edward and Singhal, Pankhuri and Gerych, Walter and Tang, Shengpu and Agrawal, Monica and Subbaswamy, Adarsh and Sizikova, Elena and Dunn, Jessilyn and Daneshjou, Roxana and Sarker, Tasmie and McDermott, Matthew and Chen, Irene}, volume = {287}, series = {Proceedings of Machine Learning Research}, month = {25--27 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v287/main/assets/park25a/park25a.pdf}, url = {https://proceedings.mlr.press/v287/park25a.html}, abstract = {Accurate electrocardiogram (ECG) delineation is essential for automated cardiac diagnosis, enabling the precise identification of key waveforms such as the P wave, QRS complex, and T wave. This study presents the first comprehensive benchmarking of neural network-based semantic segmentation models for ECG delineation, evaluating their accuracy, resource efficiency, and robustness across both public and private datasets. Our results demonstrate that convolutional neural network (CNN)-based approaches consistently achieve superior accuracy compared to other network architectures. Additionally, we observed the presence of fragmented segments in the delineation results. To address this issue, we explored post-processing techniques to consolidate or eliminate fragmented segments using an optimal configuration, leading to performance improvements. Furthermore, by analyzing performance variations across different waveform labels, we provide critical insights into key considerations for ECG segmentation tasks. Notably, our findings also reveal that larger model sizes do not necessarily correlate with better performance. Based on our findings, we propose a set of practical guidelines for leveraging segmentation models in ECG delineation, offering valuable direction for future research and clinical applications.} }
Endnote
%0 Conference Paper %T Benchmarking ECG Delineation using Deep Neural Network-based Semantic Segmentation Models %A Jaeho Park %A TaeJun Park %A Joon-myoung Kwon %A Yong-Yeon Jo %B Proceedings of the sixth Conference on Health, Inference, and Learning %C Proceedings of Machine Learning Research %D 2025 %E Xuhai Orson Xu %E Edward Choi %E Pankhuri Singhal %E Walter Gerych %E Shengpu Tang %E Monica Agrawal %E Adarsh Subbaswamy %E Elena Sizikova %E Jessilyn Dunn %E Roxana Daneshjou %E Tasmie Sarker %E Matthew McDermott %E Irene Chen %F pmlr-v287-park25a %I PMLR %P 63--88 %U https://proceedings.mlr.press/v287/park25a.html %V 287 %X Accurate electrocardiogram (ECG) delineation is essential for automated cardiac diagnosis, enabling the precise identification of key waveforms such as the P wave, QRS complex, and T wave. This study presents the first comprehensive benchmarking of neural network-based semantic segmentation models for ECG delineation, evaluating their accuracy, resource efficiency, and robustness across both public and private datasets. Our results demonstrate that convolutional neural network (CNN)-based approaches consistently achieve superior accuracy compared to other network architectures. Additionally, we observed the presence of fragmented segments in the delineation results. To address this issue, we explored post-processing techniques to consolidate or eliminate fragmented segments using an optimal configuration, leading to performance improvements. Furthermore, by analyzing performance variations across different waveform labels, we provide critical insights into key considerations for ECG segmentation tasks. Notably, our findings also reveal that larger model sizes do not necessarily correlate with better performance. Based on our findings, we propose a set of practical guidelines for leveraging segmentation models in ECG delineation, offering valuable direction for future research and clinical applications.
APA
Park, J., Park, T., Kwon, J. & Jo, Y.. (2025). Benchmarking ECG Delineation using Deep Neural Network-based Semantic Segmentation Models. Proceedings of the sixth Conference on Health, Inference, and Learning, in Proceedings of Machine Learning Research 287:63-88 Available from https://proceedings.mlr.press/v287/park25a.html.

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