Spatio Temporal Filtering of Multi-lead ECG Signals for Atrial Arrhythmia Classification
Atrial fibrillation (AF) is the most common cardiac arrhythmia and increases the risk of suffering stroke. Some people with AF do not have symptoms, so, its diagnosis can be difficult, especially in early stages of the disease. In this paper, we propose the use of the spatio-Temporal filter (STF) to...
- Autores:
-
Giraldo-Guzman, Jader
Contreras-Ortiz, Sonia H.
Castells, Francisco
Kotas, Marian
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12140
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12140
- Palabra clave:
- Atrial fibrillation
ECG signal processing
P wave
QRST cancellation
Spatio-Temporal filtering
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Spatio Temporal Filtering of Multi-lead ECG Signals for Atrial Arrhythmia Classification |
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Spatio Temporal Filtering of Multi-lead ECG Signals for Atrial Arrhythmia Classification |
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Spatio Temporal Filtering of Multi-lead ECG Signals for Atrial Arrhythmia Classification Atrial fibrillation ECG signal processing P wave QRST cancellation Spatio-Temporal filtering |
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Spatio Temporal Filtering of Multi-lead ECG Signals for Atrial Arrhythmia Classification |
| title_full |
Spatio Temporal Filtering of Multi-lead ECG Signals for Atrial Arrhythmia Classification |
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Spatio Temporal Filtering of Multi-lead ECG Signals for Atrial Arrhythmia Classification |
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Spatio Temporal Filtering of Multi-lead ECG Signals for Atrial Arrhythmia Classification |
| title_sort |
Spatio Temporal Filtering of Multi-lead ECG Signals for Atrial Arrhythmia Classification |
| dc.creator.fl_str_mv |
Giraldo-Guzman, Jader Contreras-Ortiz, Sonia H. Castells, Francisco Kotas, Marian |
| dc.contributor.author.none.fl_str_mv |
Giraldo-Guzman, Jader Contreras-Ortiz, Sonia H. Castells, Francisco Kotas, Marian |
| dc.subject.keywords.spa.fl_str_mv |
Atrial fibrillation ECG signal processing P wave QRST cancellation Spatio-Temporal filtering |
| topic |
Atrial fibrillation ECG signal processing P wave QRST cancellation Spatio-Temporal filtering |
| description |
Atrial fibrillation (AF) is the most common cardiac arrhythmia and increases the risk of suffering stroke. Some people with AF do not have symptoms, so, its diagnosis can be difficult, especially in early stages of the disease. In this paper, we propose the use of the spatio-Temporal filter (STF) to characterize atrial activity in ECG recordings and distinguish between normal sinus rhythm (NSR) and atrial arrhythmias. This method allows the effective detection of P waves when they are synchronized with QRS complexes. The distances from the QRS complexes to the detected P waves are characterized by seven dispersion metrics that are used as inputs to three clustering algorithms. The results show classification accuracy of up to 98.88% of NSR and atrial arrhythmias. |
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2021 |
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2021-10 |
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2023-07-18T19:31:46Z |
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2023-07-18T19:31:46Z |
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2023-07 |
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J. Giraldo-Guzman, S. H. Contreras-Ortiz, F. Castells and M. Kotas, "Spatio Temporal Filtering of Multi-lead ECG Signals for Atrial Arrhythmia Classification," 2021 IEEE 2nd International Congress of Biomedical Engineering and Bioengineering (CI-IB&BI), Bogota D.C., Colombia, 2021, pp. 1-6, doi: 10.1109/CI-IBBI54220.2021.9626098. |
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https://hdl.handle.net/20.500.12585/12140 |
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10.1109/CI-IBBI54220.2021.9626098 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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J. Giraldo-Guzman, S. H. Contreras-Ortiz, F. Castells and M. Kotas, "Spatio Temporal Filtering of Multi-lead ECG Signals for Atrial Arrhythmia Classification," 2021 IEEE 2nd International Congress of Biomedical Engineering and Bioengineering (CI-IB&BI), Bogota D.C., Colombia, 2021, pp. 1-6, doi: 10.1109/CI-IBBI54220.2021.9626098. 10.1109/CI-IBBI54220.2021.9626098 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12140 |
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eng |
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eng |
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Giraldo-Guzman, Jader723ee2df-784d-45f1-87c6-49e243e49827Contreras-Ortiz, Sonia H.1d56d7f5-97c9-4429-b47d-48ebe97de2a8Castells, Francisco7fe49a07-6f9d-4462-adda-2fc09a85104aKotas, Marian115f4d30-ba45-4bf6-bf66-152b622af835Colombia2023-07-18T19:31:46Z2023-07-18T19:31:46Z2021-102023-07J. Giraldo-Guzman, S. H. Contreras-Ortiz, F. Castells and M. Kotas, "Spatio Temporal Filtering of Multi-lead ECG Signals for Atrial Arrhythmia Classification," 2021 IEEE 2nd International Congress of Biomedical Engineering and Bioengineering (CI-IB&BI), Bogota D.C., Colombia, 2021, pp. 1-6, doi: 10.1109/CI-IBBI54220.2021.9626098.https://hdl.handle.net/20.500.12585/1214010.1109/CI-IBBI54220.2021.9626098Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarAtrial fibrillation (AF) is the most common cardiac arrhythmia and increases the risk of suffering stroke. Some people with AF do not have symptoms, so, its diagnosis can be difficult, especially in early stages of the disease. In this paper, we propose the use of the spatio-Temporal filter (STF) to characterize atrial activity in ECG recordings and distinguish between normal sinus rhythm (NSR) and atrial arrhythmias. This method allows the effective detection of P waves when they are synchronized with QRS complexes. The distances from the QRS complexes to the detected P waves are characterized by seven dispersion metrics that are used as inputs to three clustering algorithms. The results show classification accuracy of up to 98.88% of NSR and atrial arrhythmias.application/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf22021 IEEE 2nd International Congress of Biomedical Engineering and Bioengineering, CI-IB and BI 2021Spatio Temporal Filtering of Multi-lead ECG Signals for Atrial Arrhythmia Classificationinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Atrial fibrillationECG signal processingP waveQRST cancellationSpatio-Temporal filteringCartagena de IndiasCardiovascular Diseases. Cited 1008 times. W. H. Organization lhttp://www.who.int/mediacentre/factsheets/fs317/en/2017Jl, R.V.U. (1983) Sage Risk of Recurrent Stroke in Patients with Atrial Fibrillation and Non-valvular Heart Disease StrokeCotter, P.E., Martin, M.P.J., Ring, L., Warburton, E.A., Belham, M., Pugh, P.J. Incidence of atrial fibrillation detected by implantable loop recorders in unexplained stroke (2013) Neurology, 80 (17), pp. 1546-1550. Cited 203 times. doi: 10.1212/WNL.0b013e31828f1828Jiménez-Serrano, S., Yagüe-Mayans, J., Simarro-Mondéjar, E., Calvo, C.J., Castells, F., Millet, J. Atrial fibrillation detection using feedforward neural networks and automatically extracted signal features (2017) Computing in Cardiology, 44, pp. 1-4. Cited 20 times. http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000157 doi: 10.22489/CinC.2017.341-131Bashar, S.K., DIng, E., Albuquerque, D., Winter, M., Binici, S., Walkey, A.J., McManus, D.D., (...), Chon, K.H. Atrial Fibrillation Detection in ICU Patients: A Pilot Study on MIMIC III Data∗ (Open Access) (2019) Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, art. no. 8856496, pp. 298-301. Cited 10 times. ISBN: 978-153861311-5 doi: 10.1109/EMBC.2019.8856496Andersen, R.S., Poulsen, E.S., Puthusserypady, S. A novel approach for automatic detection of Atrial Fibrillation based on Inter Beat Intervals and Support Vector Machine (2017) Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, art. no. 8037253, pp. 2039-2042. Cited 25 times. ISBN: 978-150902809-2 doi: 10.1109/EMBC.2017.8037253Mei, Z., Gu, X., Chen, H., Chen, W. Automatic atrial fibrillation detection based on heart rate variability and spectral features (Open Access) (2018) IEEE Access, 6, art. no. 8468160, pp. 53566-53575. Cited 30 times. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639 doi: 10.1109/ACCESS.2018.2871220Islam, S., Ammour, N., Alajlan, N. Atrial fibrillation detection with multiparametric rr interval feature and machine learning technique (2017) , pp. 1-5.Kruger, G.H., Latchamsetty, R., Langhals, N.B., Yokokawa, M., Chugh, A., Morady, F., Oral, H., (...), Berenfeld, O. Bimodal classification algorithm for atrial fibrillation detection from m-health ECG recordings (2019) Computers in Biology and Medicine, 104, pp. 310-318. Cited 15 times. www.elsevier.com/locate/compbiomed doi: 10.1016/j.compbiomed.2018.11.016Ródenas, J., García, M., Alcaraz, R., Rieta, J.J. Wavelet entropy automatically detects episodes of atrial fibrillation from single-lead electrocardiograms (2015) Entropy, 17 (9), pp. 6179-6199. Cited 48 times. http://www.mdpi.com/1099-4300/17/9/6179/pdf doi: 10.3390/e17096179Garcia Teruel, M., Rieta Ibañez, J.J., Alcaraz Martinez, R., Rodenas Garcia, J. Application of the relative wavelet energy to 1 heart rate independent detection of atrial fibrillation (2016)Ladavich, S., Ghoraani, B. Rate-independent detection of atrial fibrillation by statistical modeling of atrial activity (Open Access) (2015) Biomedical Signal Processing and Control, 18, pp. 274-281. Cited 113 times. http://www.elsevier.com/wps/find/journalbibliographicinfo.cws_home/706718/description#bibliographicinfo doi: 10.1016/j.bspc.2015.01.007He, R., Wang, K., Zhao, N., Liu, Y., Yuan, Y., Li, Q., Zhang, H. Automatic detection of atrial fibrillation based on continuous wavelet transform and 2D convolutional neural networks (2018) Frontiers in Physiology, 9 (AUG), art. no. 1206. Cited 79 times. https://www.frontiersin.org/articles/10.3389/fphys.2018.01206/full doi: 10.3389/fphys.2018.01206Xia, Y., Wulan, N., Wang, K., Zhang, H. Detecting atrial fibrillation by deep convolutional neural networks (2018) Computers in Biology and Medicine, 93, pp. 84-92. Cited 213 times. www.elsevier.com/locate/compbiomed doi: 10.1016/j.compbiomed.2017.12.007Zaidi, S.H., Sheikh, S.-A.A., Akhtar, I., Zaidi, T. Differentiation between Atrial Fibrillation and Atrial Flutter using 1D Poincare Maps based on endocardial bipolar intracardiac electrograms extracted from the Right Atria (2016) Proceedings of 2016 13th International Bhurban Conference on Applied Sciences and Technology, IBCAST 2016, art. no. 7429858, pp. 77-84. ISBN: 978-146739127-6 doi: 10.1109/IBCAST.2016.7429858Razzaq, N., Sheikh, S.-A.A., Zaidi, T., Akhtar, I., Ahmed, S.H. Automated differentiation between normal sinus rhythm, atrial tachycardia, atrial flutter and atrial fibrillation during electrophysiology (2017) Proceedings - 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering, BIBE 2017, 2018-January, pp. 266-272. Cited 3 times. ISBN: 978-153861324-5 doi: 10.1109/BIBE.2017.00-43Fujita, H., Cimr, D. Computer Aided detection for fibrillations and flutters using deep convolutional neural network (Open Access) (2019) Information Sciences, 486, pp. 231-239. Cited 110 times. http://www.journals.elsevier.com/information-sciences/ doi: 10.1016/j.ins.2019.02.065Kotas, M., Jezewski, J., Horoba, K., Matonia, A. Application of spatio-temporal filtering to fetal electrocardiogram enhancement (2011) Computer Methods and Programs in Biomedicine, 104 (1), pp. 1-9. Cited 45 times. doi: 10.1016/j.cmpb.2010.07.004Giraldo-Guzmán, J., Kotas, M., Piela, M., Castells, F., Łęski, J.M., Contreras-Ortiz, S.H. Application of spatio-temporal filtering for atrial activity waveforms enhancement (2019) ACM International Conference Proceeding Series, pp. 67-72. Cited 2 times. http://portal.acm.org/ ISBN: 978-145037243-5 doi: 10.1145/3365245.3365262Leski, J.M., Kotas, M. Hierarchical clustering with planar segments as prototypes (Open Access) (2015) Pattern Recognition Letters, 54, pp. 1-10. 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