Abstract
We propose an algorithm for electrocardiogram (ECG) segmentation using a UNet-like full-convolutional neural network. The algorithm receives an arbitrary sampling rate ECG signal as an input, and gives a list of onsets and offsets of P and T waves and QRS complexes as output. Our method of segmentation differs from others in speed, a small number of parameters and a good generalization: it is adaptive to different sampling rates and it is generalized to various types of ECG monitors. The proposed approach is superior to other state-of-the-art segmentation methods in terms of quality. In particular, F1-measures for detection of onsets and offsets of P and T waves and for QRS-complexes are at least 97.8%, 99.5%, and 99.9%, respectively.
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References
Association for the Advancement of Medical Instrumentation. NSI/AAMI EC57:1998/(R)2008 (Revision of AAMI ECAR:1987) (1999)
De Boor, C.: A Practical Guide to Splines. Springer, New York (1978)
Bote, J.M., Recas, J., Rincon, F., Atienza, D., Hermida, R.: A modular low-complexity ECG delineation algorithm for real-time embedded systems. IEEE J. Biomed. Health Inform. 22, 429–441 (2017)
Kalyakulina, A.I., Yusipov, I.I., Moskalenko, V.A., Nikolskiy, A.V., Kozlov, A.A., Zolotykh, N.Y., Ivanchenko, M.V.: Finding morphology points of electrocardiographic-signal waves using wavelet analysis. Radiophys. Quantum Electron. 61(8–9), 689–703 (2019)
Kalyakulina, A.I., Yusipov, I.I., Moskalenko, V.A., Nikolskiy, A.V., Kozlov, A.A., Kosonogov, K.A., Zolotykh, N.Yu., Ivanchenko, M.V.: LU electrocardiography database: a new open-access validation tool for delineation algorithms. arXiv:1809.03393 (2018)
Di Marco, L.Y., Lorenzo, C.: A wavelet-based ECG delineation algorithm for 32-bit integer online processing. Biomed. Eng. Online 10(1), 23 (2011)
Li, C., Zheng, C., Tai, C.: Detection of ECG characteristic points using wavelet transforms. IEEE Trans. Biomed. Eng. 42(1), 21–28 (1995)
Martinez, A., Alcaraz, R., Rieta, J.J.: Automatic electrocardiogram delineator based on the phasor transform of single lead recordings. In: Computing in Cardiology, pp. 987–990. IEEE (2010)
Rincon, F., Recas, J., Khaled, N., Atienza, D.: Development and evaluation of multilead wavelet-based ECG delineation algorithms for embedded wireless sensor nodes. IEEE Trans. Inf. Technol. Biomed. 15, 854–863 (2011)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer, Cham (2015)
Sereda, I., Alekseev, S., Koneva, A., Kataev, R., Osipov G.: ECG segmentation by neural networks: errors and correction. arXiv:1812.10386 (2018)
Acknowledgement
The authors are grateful to the referee for valuable suggestions and comments. The work is supported by the Ministry of Education and Science of Russian Federation (project 14.Y26.31.0022).
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Moskalenko, V., Zolotykh, N., Osipov, G. (2020). Deep Learning for ECG Segmentation. In: Kryzhanovsky, B., Dunin-Barkowski, W., Redko, V., Tiumentsev, Y. (eds) Advances in Neural Computation, Machine Learning, and Cognitive Research III. NEUROINFORMATICS 2019. Studies in Computational Intelligence, vol 856. Springer, Cham. https://doi.org/10.1007/978-3-030-30425-6_29
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DOI: https://doi.org/10.1007/978-3-030-30425-6_29
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