ARCHIVES
Year 2026 · Volume 5 · Issue 3
Original Article
A Hybrid Segmentation-Classification Framework for ECG Beat Recognition Using U‑Net and ResNet
Chia-Wei Chang1
1 Department of Electronic Engineering, Chienkuo Technology University, Changhua City, Taiwan.
Published Online: September-December 2026
Pages: 320-329
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260503038References
1. Pan J, Tompkins WJ. A real-time QRS detection algorithm. IEEE Transactions on Biomedical Engineering. 1985;BME-32(3):230–236. doi:10.1109/TBME.1985.325532
2. Phukpattaranont P. QRS detection algorithm based on the quadratic filter. Expert Systems with Applications. 2015;42(11):4867–4877. doi:10.1016/j.eswa.2015.02.012
3. Dubois R, Maison-Blanche P, Quenet B, Dreyfus G. Automatic ECG wave extraction in long-term recordings using Gaussian mesa function models and nonlinear probability estimators. Computer Methods and Programs in Biomedicine. 2007;88:217–233.
4. Graja S, Boucher JM. Hidden Markov tree model applied to ECG delineation. IEEE Transactions on Instrumentation and Measurement. 2005;54:2163–2168.
5. Abrishami H, Han C, Zhou X, Campbell M, Czosek R. Supervised ECG interval segmentation using LSTM neural network. In: Proceedings of the International Conference on Bioinformatics and Computational Biology (BIOCOMP); 2018; Athens.
6. Jimenez-Perez G, Alcaine A, Camara O. Delineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks. Scientific Reports. 2021;11(1):863. doi:10.1038/s41598-020-79512-7
7. Laguna P, Mark RG, Goldberg A, Moody GB. A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. In: Computers in Cardiology 1997. IEEE; 1997. p. 673–676.
8. PhysioNet. QT Database documentation. Available from: https://physionet.org/physiobank/database/qtdb/doc/node4.html
2. Phukpattaranont P. QRS detection algorithm based on the quadratic filter. Expert Systems with Applications. 2015;42(11):4867–4877. doi:10.1016/j.eswa.2015.02.012
3. Dubois R, Maison-Blanche P, Quenet B, Dreyfus G. Automatic ECG wave extraction in long-term recordings using Gaussian mesa function models and nonlinear probability estimators. Computer Methods and Programs in Biomedicine. 2007;88:217–233.
4. Graja S, Boucher JM. Hidden Markov tree model applied to ECG delineation. IEEE Transactions on Instrumentation and Measurement. 2005;54:2163–2168.
5. Abrishami H, Han C, Zhou X, Campbell M, Czosek R. Supervised ECG interval segmentation using LSTM neural network. In: Proceedings of the International Conference on Bioinformatics and Computational Biology (BIOCOMP); 2018; Athens.
6. Jimenez-Perez G, Alcaine A, Camara O. Delineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks. Scientific Reports. 2021;11(1):863. doi:10.1038/s41598-020-79512-7
7. Laguna P, Mark RG, Goldberg A, Moody GB. A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. In: Computers in Cardiology 1997. IEEE; 1997. p. 673–676.
8. PhysioNet. QT Database documentation. Available from: https://physionet.org/physiobank/database/qtdb/doc/node4.html
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