{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T01:05:19Z","timestamp":1728176719056},"reference-count":42,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T00:00:00Z","timestamp":1600992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Most methods for sudden cardiac death (SCD) prediction require long-term (24 h) electrocardiogram recordings to measure heart rate variability (HRV) indices or premature ventricular complex indices (with the heartprint method). This work aimed to identify the best combinations of HRV and heartprint indices for predicting SCD based on short-term recordings (1000 heartbeats) through a support vector machine (SVM). Eleven HRV indices and five heartprint indices were measured in 135 pairs of recordings (one before an SCD episode and another without SCD as control). SVMs (defined with a radial basis function kernel with hyperparameter optimization) were trained with this dataset to identify the 13 best combinations of indices systematically. Through 10-fold cross-validation, the best area under the curve (AUC) value as a function of \u03b3 (gamma) and cost was identified. The predictive value of the identified combinations had AUCs between 0.80 and 0.86 and accuracies between 80 and 86%. Further SVM performance tests on a different dataset of 68 recordings (33 before SCD and 35 as control) showed AUC = 0.68 and accuracy = 67% for the best combination. The developed SVM may be useful for preventing imminent SCD through early warning based on electrocardiogram (ECG) or heart rate monitoring.<\/jats:p>","DOI":"10.3390\/s20195483","type":"journal-article","created":{"date-parts":[[2020,9,25]],"date-time":"2020-09-25T05:39:33Z","timestamp":1601012373000},"page":"5483","source":"Crossref","is-referenced-by-count":10,"title":["Prediction of Sudden Cardiac Death Risk with a Support Vector Machine Based on Heart Rate Variability and Heartprint Indices"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-6722-0492","authenticated-orcid":false,"given":"Marisol","family":"Martinez-Alanis","sequence":"first","affiliation":[{"name":"Facultad de Ingenier\u00eda, Universidad An\u00e1huac M\u00e9xico, Huixquilucan 52786, Estado de Mexico, Mexico"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9986-0611","authenticated-orcid":false,"given":"Erik","family":"Bojorges-Valdez","sequence":"additional","affiliation":[{"name":"Departamento de Estudios en Ingenier\u00eda para la Innovaci\u00f3n, Universidad Iberoamericana Ciudad de M\u00e9xico, Ciudad de M\u00e9xico 01219, Mexico"}]},{"given":"Niels","family":"Wessel","sequence":"additional","affiliation":[{"name":"Department of Physics, Humboldt-Universit\u00e4t zu Berlin, 10099 Berlin, Germany"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4679-7751","authenticated-orcid":false,"given":"Claudia","family":"Lerma","sequence":"additional","affiliation":[{"name":"Departamento de Instrumentaci\u00f3n Electromec\u00e1nica, Instituto Nacional de Cardiolog\u00eda Ignacio Ch\u00e1vez, Ciudad de M\u00e9xico 14089, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1161\/CIRCRESAHA.116.304493","article-title":"Sudden cardiac death risk stratification","volume":"116","author":"Deyell","year":"2015","journal-title":"Circ. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2445","DOI":"10.1113\/JP270535","article-title":"Predicting the risk of sudden cardiac death","volume":"594","author":"Lerma","year":"2016","journal-title":"J. Physiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"S118","DOI":"10.1016\/j.jelectrocard.2007.06.023","article-title":"Global public health problem of sudden cardiac death","volume":"40","author":"Mehra","year":"2007","journal-title":"J. Electrocardiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1093\/oxfordjournals.eurheartj.a014868","article-title":"Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology","volume":"17","author":"Malik","year":"1996","journal-title":"Eur. Heart J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2058","DOI":"10.1016\/j.hrthm.2015.06.030","article-title":"SCD-HeFT: Use of R-R interval statistics for long-term risk stratification for arrhythmic sudden cardiac death","volume":"12","author":"Reinhall","year":"2015","journal-title":"Hear. Rhythm"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1093\/eurheartj\/ehn537","article-title":"Prediction of fatal or near-fatal cardiac arrhythmia events in patients with depressed left ventricular function after an acute myocardial infarction","volume":"30","author":"Huikuri","year":"2009","journal-title":"Eur. Heart J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"689","DOI":"10.2147\/TCRM.S2741","article-title":"Risk stratification for arrhythmic death in an emergency department cohort: A new method of nonlinear PD2i analysis of the ECG","volume":"4","author":"Skinner","year":"2008","journal-title":"Ther. Clin. Risk Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"671","DOI":"10.2147\/TCRM.S5568","article-title":"Comparison of linear-stochastic and nonlinear-deterministic algorithms in the analysis of 15-minute clinical ECGs to predict risk of arrhythmic death","volume":"5","author":"Skinner","year":"2009","journal-title":"Ther. Clin. Risk Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"R108","DOI":"10.1186\/cc11396","article-title":"Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability compared with the modified early warning score","volume":"16","author":"Ong","year":"2012","journal-title":"Crit. Care"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.asoc.2016.02.049","article-title":"Sudden cardiac death (SCD) prediction based on nonlinear heart rate variability features and SCD index","volume":"43","author":"Fujita","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.ijcard.2016.09.117","article-title":"Quantitative analysis of ventricular ectopic beats in short-term RR interval recordings to predict imminent ventricular tachyarrhythmia","volume":"225","author":"Lerma","year":"2016","journal-title":"Int. J. Cardiol."},{"key":"ref_12","first-page":"374","article-title":"Mean frequency of premature ventricular complexes as predictor of malignant ventricular arrhythmias","volume":"72","author":"Carrim","year":"2005","journal-title":"Mt. Sinai J. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"031901","DOI":"10.1103\/PhysRevE.66.031901","article-title":"Complex patterns of abnormal heartbeats","volume":"66","author":"Ashkenazy","year":"2002","journal-title":"Phys. Rev. E. Stat. Nonlin. Soft Matter Phys."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1304","DOI":"10.1093\/europace\/eus415","article-title":"Patterns of ectopy leading to increased risk of fatal or near-fatal cardiac arrhythmia in patients with depressed left ventricular function after an acute myocardial infarction","volume":"15","author":"Lerma","year":"2013","journal-title":"Europace"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3862","DOI":"10.1016\/j.eswa.2011.09.097","article-title":"Prediction of spontaneous ventricular tachyarrhythmia by an artificial neural network using parameters gleaned from short-term heart rate variability","volume":"39","author":"Joo","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"E215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"A125","DOI":"10.1016\/S0735-1097(04)90527-X","article-title":"1091\u2013211 Heart rate dynamics before the onset of ventricular tachyarrhythmias: Results of the cardioverter defibrillator registry MARITA","volume":"43","author":"Schirdewan","year":"2004","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s003990070035","article-title":"Nonlinear analysis of complex phenomena in cardiological data","volume":"11","author":"Wessel","year":"2000","journal-title":"Herzschrittmachertherapie Elektrophysiologie"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1007\/s11517-008-0326-y","article-title":"Ventricular arrhythmias and changes in heart rate preceding ventricular tachycardia in patients with an implantable cardioverter defibrillator","volume":"46","author":"Lerma","year":"2008","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.jelectrocard.2006.04.011","article-title":"The rule of bigeminy revisited: Analysis in sudden cardiac death syndrome","volume":"40","author":"Lerma","year":"2007","journal-title":"J. Electrocardiol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1109\/TFUZZ.2009.2029569","article-title":"SOFMLS: Online self-organizing fuzzy modified least-squares network","volume":"17","author":"Rubio","year":"2009","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"103255","DOI":"10.1109\/ACCESS.2019.2929266","article-title":"Wavelet-Based EEG Processing for Epilepsy Detection Using Fuzzy Entropy and Associative Petri Net","volume":"7","author":"Chiang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Elias, I., de Jes\u00fas Rubio, J., Martinez, D.I., Vargas, T.M., Garcia, V., Mujica-Vargas, D., Meda-Campa\u00f1a, J.A., Pacheco, J., Gutierrez, G.J., and Zacarias, A. (2020). Genetic algorithm with radial basis mapping network for the electricity consumption modeling. Appl. Sci., 10.","DOI":"10.3390\/app10124239"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.hrthm.2009.11.004","article-title":"Attenuated recovery of heart rate turbulence early after myocardial infarction identifies patients at high risk for fatal or near-fatal arrhythmic events","volume":"7","author":"Huikuri","year":"2010","journal-title":"Hear. Rhythm"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/S0167-5273(02)00139-0","article-title":"Heart rate variability before the onset of ventricular tachycardia: Differences between slow and fast arrhythmias","volume":"84","author":"Meyerfeldt","year":"2002","journal-title":"Int. J. Cardiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.ijcard.2003.04.006","article-title":"Increased variability of the coupling interval of premature ventricular beats may help to identify high-risk patients with coronary artery disease","volume":"94","author":"Sosnowski","year":"2004","journal-title":"Int. J. Cardiol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1093\/oxfordjournals.eurheartj.a062444","article-title":"Respective role of sympathetic tone and of cardiac pauses in the genesis of 62 cases of ventricular fibrillation recorded during Holter monitoring","volume":"9","author":"Leclercq","year":"1988","journal-title":"Eur. Heart J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"S163","DOI":"10.1111\/j.1540-8159.2005.00010.x","article-title":"Sympathovagal balance prior to onset of repetitive monomorphic idiopathic ventricular tachycardia","volume":"28","author":"Zimmermann","year":"2005","journal-title":"Pacing Clin. Electrophysiol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"26","DOI":"10.3389\/fphys.2013.00026","article-title":"The LF\/HF ratio does not accurately measure cardiac sympatho-vagal balance","volume":"4","author":"Billman","year":"2013","journal-title":"Front. Physiol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.1109\/TBME.2013.2275000","article-title":"Ventricular fibrillation and tachycardia classification using a machine learning approach","volume":"61","author":"Li","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_33","first-page":"98","article-title":"Investigation of Support Vector Machine To Assess Cardiac Arrhythmia","volume":"1","author":"Kumaraswamy","year":"2012","journal-title":"Int. Conf. Adv. Comput. Electr. Eng."},{"key":"ref_34","first-page":"8916","article-title":"New algorithm of mortality risk prediction for cardiovascular patients admitted in intensive care unit","volume":"8","author":"Moridani","year":"2015","journal-title":"Int. J. Clin. Exp. Med."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1928","DOI":"10.1007\/978-3-642-03882-2_512","article-title":"An arrhythmia classification method based on selected features of heart rate variability signal and support vector machine-based classifier","volume":"25","author":"Yaghouby","year":"2009","journal-title":"IFMBE Proc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1258","DOI":"10.1161\/CIRCULATIONAHA.109.902148","article-title":"Long-term recording of cardiac arrhythmias with an implantable cardiac monitor in patients with reduced ejection fraction after acute myocardial infarction: The Cardiac Arrhythmias and Risk Stratification After Acute Myocardial Infarction (CARISMA) study","volume":"122","author":"Jons","year":"2010","journal-title":"Circulation"},{"key":"ref_37","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer. [1st ed.]."},{"key":"ref_38","first-page":"18","article-title":"HRV analysis using wavelet package transform and least square support vector machine","volume":"2","author":"Kheder","year":"2008","journal-title":"Int. J. Circuits, Syst. Signal Process."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.knosys.2015.03.015","article-title":"An integrated index for detection of Sudden Cardiac Death using Discrete Wavelet Transform and nonlinear features","volume":"83","author":"Acharya","year":"2015","journal-title":"Knowl. Based Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"46324","DOI":"10.1109\/ACCESS.2020.2979141","article-title":"Novel Nonlinear Hypothesis for the Delta Parallel Robot Modeling","volume":"8","author":"Aquino","year":"2020","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"31968","DOI":"10.1109\/ACCESS.2018.2846483","article-title":"On the estimation and control of nonlinear systems with parametric uncertainties and noisy outputs","volume":"6","year":"2018","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.neucom.2019.08.095","article-title":"Hybrid neural networks for big data classification","volume":"390","author":"Zamora","year":"2020","journal-title":"Neurocomputing"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/19\/5483\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T16:04:39Z","timestamp":1720022679000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/19\/5483"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,25]]},"references-count":42,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["s20195483"],"URL":"http:\/\/dx.doi.org\/10.3390\/s20195483","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,25]]}}}
