Early detection of Ventricular Bigeminy/Trigeminy rhythms
DOI:
https://doi.org/10.33837/msj.v5i1.1525Palabras clave:
Premature Ventricular Contraction, Random Forest, Machine LearningResumen
Premature Ventricular Contraction is an arrhythmia that can be associated with several cardiac disorders that affect from 40% to 75% of the general population. Premature Ventricular Contraction occurrence is diagnosed from the Electrocardiogram. If in an Electrocardiogram one (or two) Premature Ventricular Contraction occurs between two Normal heartbeats, then there is a Ventricular Bigeminy (or Trigeminy). The prevalence of Ventricular Bigeminy/Trigeminy rhythms is associated with angina, hypertension, congestive heart failure and myocardial infarction. In this work it is proposed a new approach for these rhythms early diagnosis using Decision Tree models. The proposed approach uses the information before occurrence of Ventricular Bigeminy/Trigeminy, i.e., the number of normal and abnormal heartbeats and the heart rhythm. In order to rhythm prediction, the models obtained from Random Forest algorithm, induced by cross-validation approach, are used. Proposed approach predicted Ventricular Bigeminy/Trigeminy occurrence with accuracy, sensitivity and specificity of 98.94%, 96.28% and 99.83, respectively. Furthermore, the results showed that the Ventricular Bigeminy/Trigeminy is preceded for Normal, Atrioventricular Junctional and Paced heart rhythms in most of the examples. Besides that, it is presented a simple algorithm for decision about the occurrence of Ventricular Bigeminy/Trigeminy rhythms.
Citas
Ahn, M.S. (2013). Current concepts of premature ventricular contractions, Journal of Lifestyle Medicine, 3(1), 26–33.
Amir, M., Mappangara, I., Kabo, P., Hasanuddin, Z., Setiadji, R. & Zam, S.M. (2020). Park Algorithm as Predictor of Premature Ventricular Contraction Origin in Three-Dimensional Mapping Electrophysiological Studies, International Journal of General Medicine, 13, 1083–1092. DOI: https://doi.org/10.2147/IJGM.S275188.
Andersen, R.S., Peimankar, A. & Puthusserypady, S. (2019). A deep learning approach for real-time detection of atrial fibrillation. Expert Systems with Applications, 115, 465–473. DOI: https://doi.org/10.1016/j.eswa.2018.08.011.
Awad, M. & Khanna, R. (2015). Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, New York, Apress Open.
Breiman, L. (2001). Random Forests. Machine Learning , 45, 5–32. DOI: https://doi.org/10.1023/A:1010933404324.
Bazi, Y., Hichri, H., Alajlan, N. & Ammour, N. (2013). Premature Ventricular Contraction Ar- rhythmia Detection and Classification with Gaussian Process and S Transform, in: IEEE Fifth Int. Conference on Comput. Intell., Commun. Syst. and Networks, 36–41. DOI: https://doi.org/10.1109/CICSYN.2013.44.
Cappiello, G., Das, S., Mazomenos, E.B., Maharatna, K., Koulaouzidis, G., Morgan, J. & Puddu, P.E. (2015). A statistical index for early diagnosis of ventricular arrhythmia from the trend analysis of ECG phase-portraits, Physiological Measurement, 36(1), 107–31. DOI: https://doi.org/10.1088/0967-3334/36/1/107.
Chugh, S.N. (2012). Textbook of Clinical Electrocardiography for Postgraduates, Residents and Practicing Physicians, 3 ed., London, Jaype.
Fred, K. (2009). ECG Interpretation: From Pathophysiology to Clinical Application, Springer, New York.
Garcia, T.B. & Miller, G.T. (2004). Arrhythmia Recognition: The Art of Interpretation, Jones and Bartlett Publishers, Burlington.
Gerry, K.& Lemery, R. (2018). Fast Facts: Cardiac Arrhythmias, Health Press, Karger.
Goodfellow, S.D., Goodwin, A., Greer, R., Laussen, P.C., Mazwi, M. & Eytan, D. (2018). Towards understanding ECG rhythm classification using convolutional neural networks and attention mappings, Machine Learning for Healthcare, 85, 83-101.
Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.Ch., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K. & Stanley, H.E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23), e215–e220. DOI: https://doi.org/10.1161/01.cir.101.23.e215.
Hajeb-Mohammadalipour, S., Ahmadi, M., Shahghadami, R. & Chon, K.H. (2018). Automated Method for Discrimination of Arrhythmias Using Time, Frequency, and Nonlinear Features of Electrocardiogram Signals, Sensors 18. DOI: https://doi.org/10.3390/s18072090.
Hadia, R., Guldenring, D., Finlay, D.D., Kennedy, A., Janjua, G., Bond, R. & McLaugh-lin, J. (2017). Morphology-based detection of premature ventricular contractions, Comput. Cardiol. 44, 1–4. DOI: https://doi.org/10.22489/CinC.2017.211-260.
Harris, C.R., Millman, K.J., van der Walt, S.J., Gommers, R., Virtanen, P., et. al. (2020). Array programming with NumPy. Nature 585, 357–362. DOI: https://doi.org/10.1038/s41586-020-2649-2.
Hernández-Madrid, A., Lewalter, T., Proclemer, A., Pison, L. & Lip, G.Y.H. (2014). Remote monitoring of cardiac implantable electronic devices in Europe: results of the European Rhythm Association survey, EP Europace, 16(1), 129–132. DOI: https://doi.org/10.1093/europace/eut414.
Ho, T.K. (1998). The random subspace method for constructing decision forests, in IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832–844. DOI: https://doi.org/10.1109/34.709601.
Latchamsetty, R. & Bogun, F. (2015). Premature ventricular complexes and premature ventricular complex induced cardiomyopathy, Curr. Probl. Cardiol 40, 379–422. DOI: https://doi.org/10.1016/j.cpcardiol.2015.03.002.
Lee, H., Shin, S.Y., Seo, M., Nan, G.B. & Joo, S. (2016). Prediction of Ventricular Tachycardia One Hour before Occurrence Using Artificial Neural Networks. Scientific Reports 6. DOI: https://doi.org/10.1038/srep32390.
Li, P., Liu, C., Wang, X., Zheng, D., Li, Y.& Liu, C. (2014). A low-complexity data-adaptive approach for premature ventricular contraction recognition, Signal, Image Video Process. 8, 111–120. DOI: https://doi.org/10.1007/395s11760-013-0478-6.
McKinney, W. (2010). Data structures for statistical computing in python, Proceedings of the 9th Python in Science Conference, 445, 56–61. DOI: https://doi.org/10.25080/Majora-92bf1922-00a.
Moody, G.B. & Mark, R.G. (2001). The impact of the MIT-BIH Arrhythmia Database. IEEE Engineering in Medicine and Biology Magazine, 20(3), 45–50. DOI: https://doi.org/10.1109/51.932724.
Neubauer, S. (2007). The failing heart - an engine out of fuel, The New England Journal of Medicine, 356(11), 1140–1151. DOI: https://doi.org/10.1056/nejmra063052.
Oliveira, B.R., Abreu, C.C.E. de, Duarte, M.A.Q.& Vieira Filho, J. (2019). Geometrical features for premature ventricular contraction recognition with analytic hierarchy process based machine learning algorithms selection, Computer Methods and Programs in Biomedicine, 169, 59–69. DOI: https://doi.org/10.1016/j.cmpb.2018.12.028.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., et. al. (2011). Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research 12, 2825–2830.
Petrutiu, S., Sahakian, A.V. & Swiryn, S. (2007). Abrupt changes in fibrillatory wave characteristics at the termination of paroxysmal atrial fibrillation in humans, Europace 9(7), 466–470. DOI: https://doi.org/10.1093/europace/eum096.
Ricci, R.P., Morichelli, L., D’Onofrio, A., Calò, L., Vaccari, D., Zanotto, G., Curnis, A., Buja, G., Rovai, N. & Gargaro, A. (2013). Effectiveness of remote monitoring of CIEDs in detection and treatment of clinical and device-related cardiovascular events in daily practice: the HomeGuide Registry, EP Europace, 15, 970–977. DOI: https://doi.org/10.1093/europace/eus440.
Taye, G.T., Shim, E.B., Hwang, H.J. & Lim, K.M. (2019). Machine Learning Approach to Predict Ventricular Fibrillation Based on QRS Complex Shape, Frontiers in Physiology 10. DOI: https://doi.org/10.3389/fphys.2019.01193.
Teplitzky, B.A., McRoberts, M. & Ghanbari, H. (2020). Deep learning for comprehensive ECG annotation, Rhythm, 17(5), 881–888. DOI: https://doi.org/10.1016/j.hrthm.2020.02.015.
Tom, F. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27, 861–874. DOI: https://doi.org/10.1016/j.patrec.2005.10.010.
Tsai, C.H., Ma, H.P., Lin, Y.T., Hung, C.S., Huang, S.H., Chuang, B.L., Lin, C., Lo, M.T., Peng, C.K. & Lin, Y.H. (2020). Usefulness of rhythm complexity in heart failure detection and diagnosis. Scientific Reports 10. DOI: https://doi.org/10.1038/s41598-020-71909-8.
Weaver, W.D., Cobb, L.A.& Hallstrom, A.P. (1982). Ambulatory Arrhythmias in Resuscitated Victims of Cardiac Arrest, Circulation 66(1), 212–218. DOI: https://doi.org/10.1161/01.CIR.66.1.212
Wang, S., Cui, H., Ji, K., Zhu, C., Huang, X., Lai, Y.& Wang, S. (2021). Relationship Between Obstructive Sleep Apnea and Late Gadolinium Enhancement and Their Effect on Cardiac Arrhythmias in Patients with Hypertrophic Obstructive Cardiomyopathy. Nature and Science of Sleep, 13, 447–456. DOI: https://doi.org/10.2147/NSS.S270684.
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2022 The author(s)
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Authors who publish in this journal agree to the following terms:
a) The Authors retain the copyright and grant the journal the right to first publication, with the work simultaneously licensed under the Creative Commons Attribution License that allows the sharing of the work with acknowledgment of authorship and initial publication in this journal.
b) Authors are authorized to assume additional contracts separately, for non-exclusive distribution of the version of the work published in this journal (eg, publishing in institutional repository or as a book chapter), with acknowledgment of authorship and initial publication in this journal.
c) Authors are allowed and encouraged to publish and distribute their work online (eg in institutional repositories or on their personal page) at any point before or during the editorial process, as this can generate productive changes, as well as increase impact and citation of the published work.
