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Propose: An electrocardiogram (ECG) has been extensively used to detect rhythm disturbances. We sought to determine the accuracy of different machine learning in distinguishing abnormal ECGs from normal ones in children who were examined using a resting 12-Lead ECG machine, and we also compared the manual and automated measurement using the modular ECG Analysis System (MEANS) algorithm of ECG features.

Methods: Altogether, 10745 ECGs were recorded for students aged 6 to 18. Manual and automatic ECG features were extracted for each participant. Features were normalized using Z-score normalization and went through the student's t-test and chi-squared test to measure their relevance. We applied the Boruta algorithm for feature selection and then implemented eight classifier algorithms. The dataset was split into training (80%) and test (20%) partitions. The performance of the classifiers was evaluated on the test data (unseen data) by 1000 bootstrap, and sensitivity (SEN), specificity (SPE), AUC, and accuracy (ACC) were reported.

Results: In univariate analysis, the highest performance was heart rate and RR interval in the manual dataset and heart rate in an automated dataset with AUC of 0.72 and 0.71, respectively. The best classifiers in the manual dataset were random forest (RF) and quadratic-discriminant-analysis (QDA) with AUC, ACC, SEN, and SPE equal to 0.93, 0.98, 0.69, 0.99, and 0.90, 0.95, 0.75, 0.96, respectively. In the automated dataset, QDA (AUC: 0.89, ACC:0.92, SEN:0.71, SPE:0.93) and stack learning (SL) (AUC:0.89, ACC:0.96, SEN:0.61, SPE:0.99) reached best performances.

Conclusion: This study demonstrated that the manual measurement of 12-Lead ECG features had better performance than the automated measurement (MEANS algorithm), but some classifiers had promising results in discriminating between normal and abnormal cases. Further studies can help us evaluate the applicability and efficacy of machine-learning approaches for distinguishing abnormal ECGs in community-based investigations in both adults and children.

Titel:	Comparison of Machine Learning Algorithms Using Manual/Automated Features on 12-Lead Signal Electrocardiogram Classification: A Large Cohort Study on Students Aged Between 6 to 18 Years Old.
Autor/in / Beteiligte Person:	Hajianfar, G ; Khorgami, M ; Rezaei, Y ; Amini, M ; Samiei, N ; Tabib, A ; Borji, BK ; Kalayinia, S ; Shiri, I ; Hosseini, S ; Oveisi, M
Link:	Full Text Finder (Volltext)
Zeitschrift:	Cardiovascular engineering and technology, Jg. 14 (2023-12-01), Heft 6, S. 786-800
Veröffentlichung:	New York, NY : Springer, 2023
Medientyp:	academicJournal
ISSN:	1869-4098 (electronic)
DOI:	10.1007/s13239-023-00687-x
Schlagwort:	Adult Child Humans Adolescent Cohort Studies Arrhythmias, Cardiac diagnosis Electrocardiography methods Algorithms Machine Learning
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Corporate Authors: SHED LIGHT Investigators Publication Type: Journal Article; Research Support, Non-U.S. Gov't Language: English [Cardiovasc Eng Technol] 2023 Dec; Vol. 14 (6), pp. 786-800. <i>Date of Electronic Publication: </i>2023 Oct 17. MeSH Terms: Algorithms* ; Machine Learning* ; Adult ; Child ; Humans ; Adolescent ; Cohort Studies ; Arrhythmias, Cardiac / diagnosis ; Electrocardiography / methods References: Chow, G. V., J. E. Marine, and J. L. Fleg. Epidemiology of arrhythmias and conduction disorders in older adults. Clin Geriatr Med. 28:539–553, 2012. (PMID: 10.1016/j.cger.2012.07.003231015703483564) ; Benjamin, E. J., M. J. Blaha, S. E. Chiuve, M. Cushman, S. R. Das, R. Deo, S. D. D. Ferranti, J. Floyd, M. Fornage, C. Gillespie, C. R. Isasi, M. C. Jiménez, L. C. Jordan, S. E. Judd, D. Lackland, J. H. Lichtman, L. Lisabeth, S. Liu, C. T. Longenecker, R. H. Mackey, K. Matsushita, D. Mozaffarian, M. E. Mussolino, K. Nasir, R. W. Neumar, L. Palaniappan, D. K. Pandey, R. R. Thiagarajan, M. J. Reeves, M. Ritchey, C. J. Rodriguez, G. A. Roth, W. D. Rosamond, C. Sasson, A. Towfighi, C. W. Tsao, M. B. Turner, S. S. Virani, J. H. Voeks, J. Z. Willey, J. T. Wilkins, J. H. Wu, H. M. Alger, S. S. Wong, and P. Muntner. Heart disease and stroke statistics-2017 update: a report from the American Heart Association. Circulation. 135:146-e603, 2017. (PMID: 10.1161/CIR.0000000000000485) ; Sekar, R. P. Epidemiology of arrhythmias in children. Indian Pacing Electrophysiol J. 8:S8, 2008. (PMID: 184780582363719) ; Santini, M., S. A. Di Fusco, F. Colivicchi, and A. Gargaro. Electrocardiographic characteristics, anthropometric features, and cardiovascular risk factors in a large cohort of adolescents. Europace. 20:1833–1840, 2018. (PMID: 10.1093/europace/euy073296883146212775) ; Chiu, S. N., J. K. Wang, M. H. Wu, C. W. Chang, C. A. Chen, M. T. Lin, E. T. Wu, Y. C. Hua, and H. C. Lue. Cardiac conduction disturbance detected in a pediatric population. J Pediatr. 152:85–89, 2008. (PMID: 10.1016/j.jpeds.2007.05.04418154906) ; Sox, H. C., Jr., A. M. Garber, and B. Littenberg. The resting electrocardiogram as a screening test. A clinical analysis. Ann. Internal Med. 111:489–502, 1989. (PMID: 10.7326/0003-4819-111-6-489) ; Hu, Y. H., S. Palreddy, and W. J. Tompkins. A patient-adaptable ECG beat classifier using a mixture of experts approach. IEEE Trans Bio-med Eng. 44:891–900, 1997. (PMID: 10.1109/10.623058) ; J.C.T.B. Moraes, M.O. Seixas, F.N. Vilani, E.V. Costa, A real time QRS complex classification method using Mahalanobis distance, Computers in Cardiology, 2002, pp. 201-204. ; Gonzalez Corcia, M. C., J. Sieira, G. Pappaert, C. de Asmundis, G. B. Chierchia, M. La Meir, A. Sarkozy, and P. Brugada. Implantable cardioverter-defibrillators in children and adolescents with Brugada syndrome. J Am Coll Cardiol. 71:148–157, 2018. (PMID: 10.1016/j.jacc.2017.10.08229325638) ; G.D. Clifford, C. Liu, B. Moody, H.L. Li-wei, I. Silva, Q. Li, A. Johnson, R.G. Mark, AF classification from a short single lead ECG recording: The PhysioNet/computing in cardiology challenge 2017, 2017 Computing in Cardiology (CinC), IEEE, 2017, pp. 1-4. ; Van de Loo, A., W. Arendts, and S. H. Hohnloser. Variability of QT dispersion measurements in the surface electrocardiogram in patients with acute myocardial infarction and in normal subjects. Am J Cardiol. 74:1113–1118, 1994. (PMID: 10.1016/0002-9149(94)90462-67977069) ; Massel, D. Observer variability in ECG interpretation for thrombolysis eligibility: experience and context matter. J Thromb Thrombolysis. 15:131–140, 2003. (PMID: 10.1023/B:THRO.0000011368.55165.9714739622) ; Christov, I., and G. Bortolan. Ranking of pattern recognition parameters for premature ventricular contractions classification by neural networks. Physiol Meas. 25:1281–1290, 2004. (PMID: 10.1088/0967-3334/25/5/01715535192) ; R. Singh, N. Rajpal, R. Mehta, Abnormality detection in ECG using hybrid feature extraction approach, 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), IEEE, 2018, pp. 461-466. ; Moridani, M., M. A. Zadeh, and Z. S. Mazraeh. An efficient automated algorithm for distinguishing normal and abnormal ECG signal. IRBM. 40:332–340, 2019. (PMID: 10.1016/j.irbm.2019.09.002) ; Jadhav, S., S. Nalbalwar, and A. Ghatol. Feature elimination based random subspace ensembles learning for ECG arrhythmia diagnosis. Soft Computing. 18:579–587, 2014. (PMID: 10.1007/s00500-013-1079-6) ; Venkatesan, C., P. Karthigaikumar, A. Paul, S. Satheeskumaran, and R. Kumar. ECG signal preprocessing and SVM classifier-based abnormality detection in remote healthcare applications. IEEE Access. 6:9767–9773, 2018. (PMID: 10.1109/ACCESS.2018.2794346) ; P. Tadejko, W. Rakowski, Mathematical morphology based ECG feature extraction for the purpose of heartbeat classification, 6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM'07), IEEE, 2007, pp. 322-327. ; Q. Zhao, L. Zhang, ECG feature extraction and classification using wavelet transform and support vector machines, 2005 International Conference on Neural Networks and Brain, IEEE, 2005, pp. 1089-1092. ; S. Mahmoodabadi, A. Ahmadian, M. Abolhasani, ECG feature extraction using Daubechies wavelets, Proceedings of the fifth IASTED International conference on Visualization, Imaging and Image Processing, 2005, pp. 343-348. ; Lin, H.-Y., S.-Y. Liang, Y.-L. Ho, Y.-H. Lin, and H.-P. Ma. Discrete-wavelet-transform-based noise removal and feature extraction for ECG signals. Irbm. 35:351–361, 2014. (PMID: 10.1016/j.irbm.2014.10.004) ; X. Xu, Y. Liu, ECG QRS complex detection using slope vector waveform (SVW) algorithm, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, 2004, pp. 3597-3600. ; Jager, F. Feature extraction and shape representation of ambulatory electrocardiogram using the Karhunen-lòeve transform. Electrotechn. Rev. 69:83–89, 2002. ; A. Ahmadian, S. Karimifard, H. Sadoughi, M. Abdoli, An efficient piecewise modeling of ECG signals based on Hermitian basis functions, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, 2007, pp. 3180-3183. ; Van Bemmel, J., J. Kors, and G. Van Herpen. Methods of information in medicine. Methods Inf Med. 29:346–353, 1990. (PMID: 2233382) ; Shiri, I., A. Sadr, M. Amini, Y. Salimi, A. Sanaat, A. Akhavan, B. Razeghi, S. Ferdowsi, A. Saberi, H. Arabi, M. Becker, S. Voloshynovskiy, D. Gündüz, A. Rahmim, and H. Zaidi. Decentralized distributed multi-institutional PET image segmentation using a federated deep learning framework. Clin Nucl Med. 50(4):1034–1050, 2022. ; Mohebi, M., M. Amini, M. J. Alemzadeh-Ansari, A. Alizadehasl, A. B. Rajabi, I. Shiri, H. Zaidi, and M. Orooji. Post-revascularization ejection fraction prediction for patients undergoing percutaneous coronary intervention based on myocardial perfusion SPECT imaging radiomics: a preliminary machine learning study. J Digit Imaging. 2023. https://doi.org/10.1007/s10278-023-00820-1 . (PMID: 10.1007/s10278-023-00820-13705989010407007) ; Sabouri, M., G. Hajianfar, Z. Hosseini, M. Amini, M. Mohebi, T. Ghaedian, S. Madadi, F. Rastgou, M. Oveisi, A. Bitarafan Rajabi, I. Shiri, and H. Zaidi. Myocardial perfusion SPECT imaging radiomic features and machine learning algorithms for cardiac contractile pattern recognition. J Digit Imaging. 36:497–509, 2023. (PMID: 10.1007/s10278-022-00705-936376780) ; Hajianfar, G., M. Sabouri, Y. Salimi, M. Amini, S. Bagheri, E. Jenabi, S. Hekmat, M. Maghsudi, Z. Mansouri, M. Khateri, M. Hosein Jamshidi, E. Jafari, A. Bitarafan Rajabi, M. Assadi, M. Oveisi, I. Shiri, and H. Zaidi. Artificial intelligence-based analysis of whole-body bone scintigraphy: the quest for the optimal deep learning algorithm and comparison with human observer performance. Z Med Phys. 2023. https://doi.org/10.1016/j.zemedi.2023.01.008 . (PMID: 10.1016/j.zemedi.2023.01.00836932023) ; Shiri, I., M. Amini, M. Nazari, G. Hajianfar, A. Haddadi Avval, H. Abdollahi, M. Oveisi, H. Arabi, A. Rahmim, and H. Zaidi. Impact of feature harmonization on radiogenomics analysis: prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images. Comput Biol Med. 142:105230, 2022. (PMID: 10.1016/j.compbiomed.2022.10523035051856) ; Murat, F., O. Yildirim, M. Talo, U. B. Baloglu, Y. Demir, and U. R. Acharya. Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review. Comput Biol Med. 120:103726, 2020. (PMID: 10.1016/j.compbiomed.2020.10372632421643) ; C. Ye, M.T. Coimbra, B.V. Kumar, Arrhythmia detection and classification using morphological and dynamic features of ECG signals, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, IEEE, 2010, pp. 1918-1921. ; T. Tabassum, M. Islam, An approach of cardiac disease prediction by analyzing ECG signal, 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), IEEE, 2016, pp. 1-5. ; Hosseini, S., N. Samiei, A. Tabib, H. Bakhshandeh, Y. Rezaei, M. Parsaee, F. R. Ghader, M. Moradian, M. Shojaeifard, and Z. Khajali. Prevalence of structural heart diseases detected by handheld echocardiographic device in school-age children in Iran: the SHED LIGHT study. Glob Heart. 17(1):39, 2022. (PMID: 10.5334/gh.1121358373549205369) ; Sharma, S., J. A. Drezner, A. Baggish, M. Papadakis, M. G. Wilson, J. M. Prutkin, A. La Gerche, M. J. Ackerman, M. Borjesson, J. C. Salerno, I. M. Asif, D. S. Owens, E. H. Chung, M. S. Emery, V. F. Froelicher, H. Heidbuchel, C. Adamuz, C. A. Asplund, G. Cohen, K. G. Harmon, J. C. Marek, S. Molossi, J. Niebauer, H. F. Pelto, M. V. Perez, N. R. Riding, T. Saarel, C. M. Schmied, D. M. Shipon, R. Stein, V. L. Vetter, A. Pelliccia, and D. Corrado. International recommendations for electrocardiographic interpretation in athletes. J Am Coll Cardiol. 69:1057–1075, 2017. (PMID: 10.1016/j.jacc.2017.01.01528231933) ; Rautaharju, P. M., B. Surawicz, L. S. Gettes, J. J. Bailey, R. Childers, B. J. Deal, A. Gorgels, E. W. Hancock, M. Josephson, P. Kligfield, J. A. Kors, P. Macfarlane, J. W. Mason, D. M. Mirvis, P. Okin, O. Pahlm, G. van Herpen, G. S. Wagner, and H. Wellens. AHA/ACCF/HRS recommendations for the standardization and interpretation of the electrocardiogram: part IV: the ST segment, T and U waves, and the QT interval: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society: endorsed by the International Society for Computerized Electrocardiology. Circulation. 119:e241-250, 2009. (PMID: 10.1161/CIRCULATIONAHA.108.19109619228821) ; Maron, B. J., R. A. Friedman, P. Kligfield, B. D. Levine, S. Viskin, B. R. Chaitman, P. M. Okin, J. P. Saul, L. Salberg, G. F. Van Hare, E. Z. Soliman, J. Chen, G. P. Matherne, S. F. Bolling, M. J. Mitten, A. Caplan, G. J. Balady, and P. D. Thompson. Assessment of the 12-lead ECG as a screening test for detection of cardiovascular disease in healthy general populations of young people (12–25 Years of Age): a scientific statement from the American Heart Association and the American College of Cardiology. Circulation. 130:1303–1334, 2014. (PMID: 10.1161/CIR.000000000000002525223981) ; Kligfield, P., L. S. Gettes, J. J. Bailey, R. Childers, B. J. Deal, E. W. Hancock, G. van Herpen, J. A. Kors, P. Macfarlane, D. M. Mirvis, O. Pahlm, P. Rautaharju, G. S. Wagner, M. Josephson, J. W. Mason, P. Okin, B. Surawicz, and H. Wellens. Recommendations for the standardization and interpretation of the electrocardiogram: part I: the electrocardiogram and its technology a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society endorsed by the International Society for Computerized Electrocardiology. J Am Coll Cardiol. 49:1109–1127, 2007. (PMID: 10.1016/j.jacc.2007.01.02417349896) ; Hancock, E. W., B. J. Deal, D. M. Mirvis, P. Okin, P. Kligfield, L. S. Gettes, J. J. Bailey, R. Childers, A. Gorgels, M. Josephson, J. A. Kors, P. Macfarlane, J. W. Mason, O. Pahlm, P. M. Rautaharju, B. Surawicz, G. van Herpen, G. S. Wagner, and H. Wellens. AHA/ACCF/HRS recommendations for the standardization and interpretation of the electrocardiogram: part V: electrocardiogram changes associated with cardiac chamber hypertrophy: a scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society: endorsed by the International Society for Computerized Electrocardiology. Circulation. 119:e251-261, 2009. (PMID: 10.1161/CIRCULATIONAHA.108.19109719228820) ; Ackerman, M. J., S. G. Priori, S. Willems, C. Berul, R. Brugada, H. Calkins, A. J. Camm, P. T. Ellinor, M. Gollob, R. Hamilton, R. E. Hershberger, D. P. Judge, H. Le Marec, W. J. McKenna, E. Schulze-Bahr, C. Semsarian, J. A. Towbin, H. Watkins, A. Wilde, C. Wolpert, and D. P. Zipes. HRS/EHRA expert consensus statement on the state of genetic testing for the channelopathies and cardiomyopathies: this document was developed as a partnership between the Heart Rhythm Society (HRS) and the European Heart Rhythm Association (EHRA). Europace. 13:1077–1109, 2011. (PMID: 10.1093/europace/eur24521810866) ; Shrout, P. E., and J. L. Fleiss. Intraclass correlations: uses in assessing rater reliability. Psychol Bull. 86:420, 1979. (PMID: 10.1037/0033-2909.86.2.42018839484) ; Bazett, H. C. An analysis of the time relations of electrocardiograms. Heart. 7:353–370, 1920. ; Kursa, M. B., and W. R. Rudnicki. Feature selection with the Boruta package. J Stat Softw. 36:1–13, 2010. (PMID: 10.18637/jss.v036.i11) ; R.C. Team, R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2012, 2021. ; Ribeiro, A. H., M. H. Ribeiro, G. M. Paixão, D. M. Oliveira, P. R. Gomes, J. A. Canazart, M. P. Ferreira, C. R. Andersson, P. W. Macfarlane, and W. Meira Jr. Automatic diagnosis of the 12-lead ECG using a deep neural network. Nat Commun. 11:1–9, 2020. Grant Information: 962126 National Institute for Medical Research Development Contributed Indexing: Keywords: Classification; Electrocardiogram; Machine learning; Manual/automated features; Pediatric Entry Date(s): Date Created: 20231017 Date Completed: 20231222 Latest Revision: 20240320 Update Code: 20240320

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Comparison of Machine Learning Algorithms Using Manual/Automated Features on 12-Lead Signal Electrocardiogram Classification: A Large Cohort Study on Students Aged Between 6 to 18 Years Old.