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A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

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A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

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Huerta Herraiz, Á.; Martínez-Rodrigo, A.; Bertomeu-González, V.; Quesada, A.; Rieta, JJ.; Alcaraz, R. (2020). A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices. Entropy. 22(7):1-17. https://doi.org/10.3390/e22070733

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Title: A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices
Author: Huerta Herraiz, Álvaro Martínez-Rodrigo, Arturo Bertomeu-González, Vicente Quesada, Aurelio Rieta, J J Alcaraz, Raúl
UPV Unit: Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica
Issued date:
Abstract:
[EN] Atrial fibrillation (AF) is the most common heart rhythm disturbance in clinical practice. It often starts with asymptomatic and very short episodes, which are extremely difficult to detect without long-term monitoring ...[+]
Subjects: Atrial fibrillation , Continuous wavelet transform , Convolutional neural network , Deep learning , Quality assessment , Single-lead ECG
Copyrigths: Reconocimiento (by)
Source:
Entropy. (issn: 1099-4300 )
DOI: 10.3390/e22070733
Publisher:
MDPI AG
Publisher version: https://doi.org/10.3390/e22070733
Project ID:
JCCM/SBPLY/17/180501/000411
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-83952-C3-1-R/ES/ESTUDIO MULTICENTRICO PARA LA EVALUACION DEL SUSTRATO ARRITMOGENICO EN PACIENTES CON FIBRILACION AURICULAR. APLICACION A LA ABLACION POR CATETER/
GV/AICO/2019/036
Thanks:
This research has been supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha and AICO/2019/036 from Generalitat Valenciana.
Type: Artículo

References

Lippi, G., Sanchis-Gomar, F., & Cervellin, G. (2020). Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge. International Journal of Stroke, 16(2), 217-221. doi:10.1177/1747493019897870

Krijthe, B. P., Kunst, A., Benjamin, E. J., Lip, G. Y. H., Franco, O. H., Hofman, A., … Heeringa, J. (2013). Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060. European Heart Journal, 34(35), 2746-2751. doi:10.1093/eurheartj/eht280

Colilla, S., Crow, A., Petkun, W., Singer, D. E., Simon, T., & Liu, X. (2013). Estimates of Current and Future Incidence and Prevalence of Atrial Fibrillation in the U.S. Adult Population. The American Journal of Cardiology, 112(8), 1142-1147. doi:10.1016/j.amjcard.2013.05.063 [+]
Lippi, G., Sanchis-Gomar, F., & Cervellin, G. (2020). Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge. International Journal of Stroke, 16(2), 217-221. doi:10.1177/1747493019897870

Krijthe, B. P., Kunst, A., Benjamin, E. J., Lip, G. Y. H., Franco, O. H., Hofman, A., … Heeringa, J. (2013). Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060. European Heart Journal, 34(35), 2746-2751. doi:10.1093/eurheartj/eht280

Colilla, S., Crow, A., Petkun, W., Singer, D. E., Simon, T., & Liu, X. (2013). Estimates of Current and Future Incidence and Prevalence of Atrial Fibrillation in the U.S. Adult Population. The American Journal of Cardiology, 112(8), 1142-1147. doi:10.1016/j.amjcard.2013.05.063

Khoo, C. W., Krishnamoorthy, S., Lim, H. S., & Lip, G. Y. H. (2012). Atrial fibrillation, arrhythmia burden and thrombogenesis. International Journal of Cardiology, 157(3), 318-323. doi:10.1016/j.ijcard.2011.06.088

Warmus, P., Niedziela, N., Huć, M., Wierzbicki, K., & Adamczyk-Sowa, M. (2020). Assessment of the manifestations of atrial fibrillation in patients with acute cerebral stroke – a single-center study based on 998 patients. Neurological Research, 42(6), 471-476. doi:10.1080/01616412.2020.1746508

Sposato, L. A., Cipriano, L. E., Saposnik, G., Vargas, E. R., Riccio, P. M., & Hachinski, V. (2015). Diagnosis of atrial fibrillation after stroke and transient ischaemic attack: a systematic review and meta-analysis. The Lancet Neurology, 14(4), 377-387. doi:10.1016/s1474-4422(15)70027-x

Schotten, U., Dobrev, D., Platonov, P. G., Kottkamp, H., & Hindricks, G. (2016). Current controversies in determining the main mechanisms of atrial fibrillation. Journal of Internal Medicine, 279(5), 428-438. doi:10.1111/joim.12492

Ferrari, R., Bertini, M., Blomstrom-Lundqvist, C., Dobrev, D., Kirchhof, P., Pappone, C., … Vicedomini, G. G. (2016). An update on atrial fibrillation in 2014: From pathophysiology to treatment. International Journal of Cardiology, 203, 22-29. doi:10.1016/j.ijcard.2015.10.089

Meyre, P., Blum, S., Berger, S., Aeschbacher, S., Schoepfer, H., Briel, M., … Conen, D. (2019). Risk of Hospital Admissions in Patients With Atrial Fibrillation: A Systematic Review and Meta-analysis. Canadian Journal of Cardiology, 35(10), 1332-1343. doi:10.1016/j.cjca.2019.05.024

Van Wagoner, D. R., Piccini, J. P., Albert, C. M., Anderson, M. E., Benjamin, E. J., Brundel, B., … Wehrens, X. H. T. (2015). Progress toward the prevention and treatment of atrial fibrillation: A summary of the Heart Rhythm Society Research Forum on the Treatment and Prevention of Atrial Fibrillation, Washington, DC, December 9–10, 2013. Heart Rhythm, 12(1), e5-e29. doi:10.1016/j.hrthm.2014.11.011

De Vos, C. B., Pisters, R., Nieuwlaat, R., Prins, M. H., Tieleman, R. G., Coelen, R.-J. S., … Crijns, H. J. G. M. (2010). Progression From Paroxysmal to Persistent Atrial Fibrillation. Journal of the American College of Cardiology, 55(8), 725-731. doi:10.1016/j.jacc.2009.11.040

SCHUCHERT, A., BEHRENS, G., & MEINERTZ, T. (1999). Impact of Long-Term ECG Recording on the Detection of Paroxysmal Atrial Fibrillation in Patients After an Acute Ischemic Stroke. Pacing and Clinical Electrophysiology, 22(7), 1082-1084. doi:10.1111/j.1540-8159.1999.tb00574.x

Pagola, J., Juega, J., Francisco-Pascual, J., Moya, A., Sanchis, M., Bustamante, A., … Arenillas, J. F. (2018). Yield of atrial fibrillation detection with Textile Wearable Holter from the acute phase of stroke: Pilot study of Crypto-AF registry. International Journal of Cardiology, 251, 45-50. doi:10.1016/j.ijcard.2017.10.063

Luong, D. T., Ha, N. T., & Thuan, N. D. (2019). Android Smart Phones Application in Tele-monitoring Electrocardiogram (ECG). American Journal of Biomedical Sciences, 15-21. doi:10.5099/aj190100015

Haverkamp, H. T., Fosse, S. O., & Schuster, P. (2019). Accuracy and usability of single-lead ECG from smartphones - A clinical study. Indian Pacing and Electrophysiology Journal, 19(4), 145-149. doi:10.1016/j.ipej.2019.02.006

Nagai, S., Anzai, D., & Wang, J. (2017). Motion artefact removals for wearable ECG using stationary wavelet transform. Healthcare Technology Letters, 4(4), 138-141. doi:10.1049/htl.2016.0100

Satija, U., Ramkumar, B., & Manikandan, M. S. (2018). A Review of Signal Processing Techniques for Electrocardiogram Signal Quality Assessment. IEEE Reviews in Biomedical Engineering, 11, 36-52. doi:10.1109/rbme.2018.2810957

Aboukhalil, A., Nielsen, L., Saeed, M., Mark, R. G., & Clifford, G. D. (2008). Reducing false alarm rates for critical arrhythmias using the arterial blood pressure waveform. Journal of Biomedical Informatics, 41(3), 442-451. doi:10.1016/j.jbi.2008.03.003

Bashar, S. K., Ding, E., Walkey, A. J., McManus, D. D., & Chon, K. H. (2019). Noise Detection in Electrocardiogram Signals for Intensive Care Unit Patients. IEEE Access, 7, 88357-88368. doi:10.1109/access.2019.2926199

Yoon, D., Lim, H. S., Jung, K., Kim, T. Y., & Lee, S. (2019). Deep Learning-Based Electrocardiogram Signal Noise Detection and Screening Model. Healthcare Informatics Research, 25(3), 201. doi:10.4258/hir.2019.25.3.201

Oster, J., Behar, J., Sayadi, O., Nemati, S., Johnson, A. E. W., & Clifford, G. D. (2015). Semisupervised ECG Ventricular Beat Classification With Novelty Detection Based on Switching Kalman Filters. IEEE Transactions on Biomedical Engineering, 62(9), 2125-2134. doi:10.1109/tbme.2015.2402236

Levkov, C., Mihov, G., Ivanov, R., Daskalov, I., Christov, I., & Dotsinsky, I. (2005). Removal of power-line interference from the ECG: a review of the subtraction procedure. BioMedical Engineering OnLine, 4(1). doi:10.1186/1475-925x-4-50

Luo, S., & Johnston, P. (2010). A review of electrocardiogram filtering. Journal of Electrocardiology, 43(6), 486-496. doi:10.1016/j.jelectrocard.2010.07.007

Martínez, A., Alcaraz, R., & Rieta, J. J. (2010). Application of the phasor transform for automatic delineation of single-lead ECG fiducial points. Physiological Measurement, 31(11), 1467-1485. doi:10.1088/0967-3334/31/11/005

Manikandan, M. S., & Ramkumar, B. (2014). Straightforward and robust QRS detection algorithm for wearable cardiac monitor. Healthcare Technology Letters, 1(1), 40-44. doi:10.1049/htl.2013.0019

Satija, U., Ramkumar, B., & Manikandan, M. S. (2018). An automated ECG signal quality assessment method for unsupervised diagnostic systems. Biocybernetics and Biomedical Engineering, 38(1), 54-70. doi:10.1016/j.bbe.2017.10.002

Satija, U., Ramkumar, B., & Manikandan, M. S. (2018). Automated ECG Noise Detection and Classification System for Unsupervised Healthcare Monitoring. IEEE Journal of Biomedical and Health Informatics, 22(3), 722-732. doi:10.1109/jbhi.2017.2686436

Zhang, Q., Fu, L., & Gu, L. (2019). A Cascaded Convolutional Neural Network for Assessing Signal Quality of Dynamic ECG. Computational and Mathematical Methods in Medicine, 2019, 1-12. doi:10.1155/2019/7095137

Xu, X., Wei, S., Ma, C., Luo, K., Zhang, L., & Liu, C. (2018). Atrial Fibrillation Beat Identification Using the Combination of Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks. Journal of Healthcare Engineering, 2018, 1-8. doi:10.1155/2018/2102918

Al Rahhal, M. M., Bazi, Y., Al Zuair, M., Othman, E., & BenJdira, B. (2018). Convolutional Neural Networks for Electrocardiogram Classification. Journal of Medical and Biological Engineering, 38(6), 1014-1025. doi:10.1007/s40846-018-0389-7

He, R., Wang, K., Zhao, N., Liu, Y., Yuan, Y., Li, Q., & Zhang, H. (2018). Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks. Frontiers in Physiology, 9. doi:10.3389/fphys.2018.01206

Yildirim, O., Talo, M., Ay, B., Baloglu, U. B., Aydin, G., & Acharya, U. R. (2019). Automated detection of diabetic subject using pre-trained 2D-CNN models with frequency spectrum images extracted from heart rate signals. Computers in Biology and Medicine, 113, 103387. doi:10.1016/j.compbiomed.2019.103387

SINGH, S. A., & MAJUMDER, S. (2019). A NOVEL APPROACH OSA DETECTION USING SINGLE-LEAD ECG SCALOGRAM BASED ON DEEP NEURAL NETWORK. Journal of Mechanics in Medicine and Biology, 19(04), 1950026. doi:10.1142/s021951941950026x

Byeon, Y.-H., Pan, S.-B., & Kwak, K.-C. (2019). Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics. Sensors, 19(4), 935. doi:10.3390/s19040935

Clifford, G., Liu, C., Moody, B., Lehman, L., Silva, I., Li, Q., … Mark, R. (2017). AF Classification from a Short Single Lead ECG Recording: the Physionet Computing in Cardiology Challenge 2017. 2017 Computing in Cardiology Conference (CinC). doi:10.22489/cinc.2017.065-469

Redmond, S. J., Xie, Y., Chang, D., Basilakis, J., & Lovell, N. H. (2012). Electrocardiogram signal quality measures for unsupervised telehealth environments. Physiological Measurement, 33(9), 1517-1533. doi:10.1088/0967-3334/33/9/1517

Li, T., & Zhou, M. (2016). ECG Classification Using Wavelet Packet Entropy and Random Forests. Entropy, 18(8), 285. doi:10.3390/e18080285

Khorrami, H., & Moavenian, M. (2010). A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification. Expert Systems with Applications, 37(8), 5751-5757. doi:10.1016/j.eswa.2010.02.033

Lyon, A., Mincholé, A., Martínez, J. P., Laguna, P., & Rodriguez, B. (2018). Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances. Journal of The Royal Society Interface, 15(138), 20170821. doi:10.1098/rsif.2017.0821

Mincholé, A., & Rodriguez, B. (2019). Artificial intelligence for the electrocardiogram. Nature Medicine, 25(1), 22-23. doi:10.1038/s41591-018-0306-1

Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187, 27-48. doi:10.1016/j.neucom.2015.09.116

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. doi:10.1145/3065386

Li, Q., Rajagopalan, C., & Clifford, G. D. (2014). A machine learning approach to multi-level ECG signal quality classification. Computer Methods and Programs in Biomedicine, 117(3), 435-447. doi:10.1016/j.cmpb.2014.09.002

Zhao, Z., & Zhang, Y. (2018). SQI Quality Evaluation Mechanism of Single-Lead ECG Signal Based on Simple Heuristic Fusion and Fuzzy Comprehensive Evaluation. Frontiers in Physiology, 9. doi:10.3389/fphys.2018.00727

Moeyersons, J., Smets, E., Morales, J., Villa, A., De Raedt, W., Testelmans, D., … Varon, C. (2019). Artefact detection and quality assessment of ambulatory ECG signals. Computer Methods and Programs in Biomedicine, 182, 105050. doi:10.1016/j.cmpb.2019.105050

Clifford, G. D., Behar, J., Li, Q., & Rezek, I. (2012). Signal quality indices and data fusion for determining clinical acceptability of electrocardiograms. Physiological Measurement, 33(9), 1419-1433. doi:10.1088/0967-3334/33/9/1419

Orphanidou, C., Bonnici, T., Charlton, P., Clifton, D., Vallance, D., & Tarassenko, L. (2014). Signal Quality Indices for the Electrocardiogram and Photoplethysmogram: Derivation and Applications to Wireless Monitoring. IEEE Journal of Biomedical and Health Informatics, 1-1. doi:10.1109/jbhi.2014.2338351

Hayn, D., Jammerbund, B., & Schreier, G. (2012). QRS detection based ECG quality assessment. Physiological Measurement, 33(9), 1449-1461. doi:10.1088/0967-3334/33/9/1449

Casey, S., Avalos, G., & Dowling, M. (2018). Critical care nurses’ knowledge of alarm fatigue and practices towards alarms: A multicentre study. Intensive and Critical Care Nursing, 48, 36-41. doi:10.1016/j.iccn.2018.05.004

Nattel, S., Guasch, E., Savelieva, I., Cosio, F. G., Valverde, I., Halperin, J. L., … Camm, A. J. (2014). Early management of atrial fibrillation to prevent cardiovascular complications. European Heart Journal, 35(22), 1448-1456. doi:10.1093/eurheartj/ehu028

Zhao, Z., Liu, C., Li, Y., Li, Y., Wang, J., Lin, B.-S., & Li, J. (2019). Noise Rejection for Wearable ECGs Using Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks. IEEE Access, 7, 34060-34067. doi:10.1109/access.2019.2900719

Petrėnas, A., Marozas, V., & Sörnmo, L. (2015). Low-complexity detection of atrial fibrillation in continuous long-term monitoring. Computers in Biology and Medicine, 65, 184-191. doi:10.1016/j.compbiomed.2015.01.019

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