A support vector machine for predicting defibrillation outcomes from waveform metrics

Resuscitation

Volumn 85, Issue 3, 2014, Pages 343-349

  Howe, Andrew ^a   Escalona, Omar J ^b   Di Maio, Rebecca ^c   Massot, Bertrand ^d   Cromie, Nick A ^e   Darragh, Karen M ^e   Adgey, Jennifer ^e   McEneaney, David J ^a  

^a CRAIGAVON AREA HOSPITAL   (United Kingdom)

^b UNIVERSITY OF ULSTER   (United Kingdom)

^c HEARTSINE TECHNOLOGIES LTD   (United Kingdom)

^d INSTITUT DES NANOTECHNOLOGIES DE LYON   (France)

^e ROYAL VICTORIA HOSPITAL   (United Kingdom)

Author keywords

Cardiac arrest; CPR; Defibrillation; Resuscitation therapy; Support vector machine (SVM); VF waveform metrics

Indexed keywords

ADULT; ARTICLE; CARDIOVASCULAR PARAMETERS; CARDIOVERSION; CLINICAL ARTICLE; DEFIBRILLATION; FEMALE; HEART ARREST; HEART VENTRICLE FIBRILLATION; HUMAN; MALE; MEASUREMENT; MEASUREMENT ERROR; MIDDLE AGED; OUT OF HOSPITAL CARDIAC ARREST; OUTCOME ASSESSMENT; PERFORMANCE; PREDICTION; PRIORITY JOURNAL; PROGNOSIS; RETROSPECTIVE STUDY; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; WAVEFORM; WAVEFORM METRIC; COMPLICATION; PATHOPHYSIOLOGY; TREATMENT OUTCOME; VENTRICULAR FIBRILLATION;

ELECTRIC COUNTERSHOCK; FEMALE; HEART ARREST; HUMANS; MALE; MIDDLE AGED; PROGNOSIS; RETROSPECTIVE STUDIES; SUPPORT VECTOR MACHINES; TREATMENT OUTCOME; VENTRICULAR FIBRILLATION;

EID: 84893942899     PISSN: 03009572     EISSN: 18731570     Source Type: Journal    
DOI: 10.1016/j.resuscitation.2013.11.021     Document Type: Article

References (30)

1. Abrams H.C., McNally B., Ong M., Moyer P.H., Dyer K.S. A composite model of survival from out-of-hospital cardiac arrest using the Cardiac Arrest Registry to Enhance Survival (CARES). Resuscitation 2013, 84:1093-1098.
2. Girotra S., Nallamothu B.K., Spertus J.A., et al. Trends in survival after in-hospital cardiac arrest. N Engl J Med 2012, 367:1912-1920.
3. Gilmore C.M., Rea T.D., Becker L.J., Eisenberg M.S. Three-phase model of cardiac arrest: time-dependent benefit of bystander cardiopulmonary resuscitation. Am J Cardiol 2006, 98:497-499.
4. Li Y., Tang W. Optimizing the timing of defibrillation: the role of ventricular fibrillation waveform analysis during cardiopulmonary resuscitation. Crit Care Clin 2012, 28:199-210.
5. Berg R.A., Hilwig R.W., Ewy G.A., et al. Precountershock cardiopulmonary resuscitation improves initial response to defibrillation from prolonged ventricular fibrillation: a randomized, controlled swine study. Crit Care Med 2004, 32:1352-1357.
6. Weisfeldt M.L., Becker L.B. Resuscitation after cardiac arrest: a 3-phase time-sensitive model. J Am Med Assoc 2002, 288:3035-3038.
7. Eftestøl T., Wik L., Sunde K., Steen P.A. Effects of cardiopulmonary resuscitation on predictors of ventricular fibrillation defibrillation success during out-of-hospital cardiac arrest. Circulation 2004, 110:10-15.
8. Callaway C.W., Menegazzi J.J. Waveform analysis of ventricular fibrillation to predict defibrillation. Curr Opin Crit Care 2005, 11:192-199.
9. Marn-Pernat A., Weil M., Tany W., et al. Optimising timing of ventricular defibrillation. Crit Care Med 2001, 29:2360-2365.
10. Li Y., Tang W. Optimising the timing of defibrillation: the role of ventricular fibrillation waveform analysis during cardiopulmonary resuscitation. Crit Care Clin 2012, 28:199-210.
11. Diaz J., Gonzalez C., Escalona O. A support vector machine for predicting spontaneous termination of paroxysmal atrial fibrillation episodes. Comput Cardiol 2006, 33:949-952.
12. Diaz J., Escalona O.J., Castro N.C., Anderson J., Glover B., Manoharan G. Predicting transthoracic defibrillation shocks outcome in the cardioversion of atrial fibrillation employing support vector machines. Comput Cardiol 2010, 37:741-744.
13. Zhang Y., Liu F., Zhao Z., Li D., Zhou X., Wang J. Studies on application of Support Vector Machine in diagnose of coronary heart disease. Proc of IEEE ICEF 2012, 10.1109/ICEF.2012.6310380.
14. Li Q., Zhao J., Zhao Y.N. Detection of ventricular fibrillation by support vector machine algorithm. International Asia conference on IEEE informatics in Control, Automation and Robotics (CAR) 2009, 287-290. 10.1109/CAR.2009.29.
15. Eftestøl T., Sunde K., Aase S.O., Husøy J.H., Steen P.A. Predicting outcome of defibrillation by spectral characterization and nonparametric classification of ventricular fibrillation in patients with out-of-hospital cardiac arrest. Circulation 2000, 102:1523-1529.
16. Hamprecht F.A., Achleitner U., Krismer A.C., et al. Fibrillation power, an alternative method of ECG spectral analysis for prediction of countershock success in a porcine model of ventricular fibrillation. Resuscitation 2001, 50:287-296.
17. Krizmarica M., Verlic M., Stiglic G., Grmec S., Kokola P. Intelligent analysis in predicting outcome of out-of-hospital cardiac arrest. Comput Methods Programs Biomed 2009, 95 S:S22-S32.
18. Shandilya S., Ward K., Kurz M., Najarian K. Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning. BMC Med Inform Decis Making 2012, 12:116. http://www.biomedcentral.com/1472-6947/12/116.
19. Boser B.E., Guyon I., Vapnik V. A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on Computational Learning Theory 1992 1992, 144-152. ACM Press.
20. Cortes C., Vapnik V. Support-vector network. Mach Learn 1995, 20:273-297.
21. Darragh K.M., Manoharan G., Di Maio R., et al. A low tilt waveform in the transthoracic defibrillation of ventricular arrhythmias during cardiac arrest. Resuscitation 2012, 83:1438-1443.
22. Nakagawa Y., Sato Y., Kojima T., et al. Electrical defibrillation outcome prediction by waveform analysis of ventricular fibrillation in cardiac arrest out of hospital patients. Tokai J Exp Clin Med 2012, 37:1-5.
23. Reed M.J., Clegg G.R., Robertson C.E. Analysing the ventricular fibrillation waveform. Resuscitation 2003, 57:11-20.
24. Shanmugasundaram M., Valles A., Kellum M.J., Ewy G.A., Indik J.H. Analysis of amplitude spectral area and slope to predict defibrillation in out of hospital cardiac arrest due to ventricular fibrillation (VF) according to VF type: recurrent versus shock-resistant. Resuscitation 2012, 83:1242-1247.
25. Lin L., Lo M.T., Ko P.C.I., Lin C., et al. Detrended fluctuation analysis predicts successful defibrillation for out-of-hospital ventricular fibrillation cardiac arrest. Resuscitation 2010, 81:297-301.
26. Indrayan A. Medical Biostatistics 2010, 250-269. Chapman & Hall/CRC, USA, ISBN-13: 978-1-58488-887-1. 2nd ed.
27. Watson J.N., Addisona P.S., Cleggb G.R., Steenc P.A., Robertsonb C.E. Practical issues in the evaluation of methods for the prediction of shock outcome success in out-of-hospital cardiac arrest patients. Resuscitation 2006, 68:51-59.
28. Povoas H.P., Weil M.H., Tang W., Bisera J., Klouche K., Barbatsis A. Predicting the success of defibrillation by electrocardiographic analysis. Resuscitation 2002, 53:77-82.
29. Sherman L., Niemann J., Youngquist S.T., Shah A.P., Rosborough J.P. Beta-blockade causes a reduction in the frequency spectrum of VF but improves resuscitation outcome: a potential limitation of quantitative waveform measures. Resuscitation 2012, 83:511-516.
30. Indik J.H., Donnerstein R.L., Hilwig R.W., et al. The influence of myocardial substrate on ventricular fibrillation waveform: a swine model of acute and postmyocardial infarction. Crit Care Med 2008, 36:2136-2142.