Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis of the Likelihood of Successful Defibrillation as a Change of Cardiopulmonary Resuscitation Transition using Support Vector Machine

서포트 벡터 머신을 이용한 심폐소생술 변이의 변화에 따른 제세동 성공률 분석

  • Jang, Seung-Jin (Department of Bimomedical Engineering, Health and Science College, Yonsei University) ;
  • Hwang, Sung-Oh (Department of Emergency Medicine, Wonju College of Medicine, Yonsei University) ;
  • Lee, Hyun-Sook (Department of Oriental Biomedical Engineering, College of Health Science, Sangji University) ;
  • Yoon, Young-Ro (Department of Bimomedical Engineering, Health and Science College, Yonsei University)
  • 장승진 (연세대학교 보건과학대학 의공학과) ;
  • 황성오 (연세대학교 원주의과대학 응급의학교실) ;
  • 이현숙 (상지대학교 보건과학대학 한방의료공학과) ;
  • 윤영로 (연세대학교 보건과학대학 의공학과)
  • Published : 2007.08.30
Download PDF
⟨ Previous Next ⟩

Abstract

Unsatisfied results of return of spontaneous circulation (ROSC) estimates were caused by the fact that the predictability of the predictors was insufficient. This unmet estimate of the predictors may be affected by transitional events due to behaviors which occur during cardiopulmonary resuscitation (CPR). We thus hypothesized that the discrepancy of ROSC estimates found in statistical characteristics due to transitional CPR events, may affect the performance of the predictors, and that the performance of the classifier dichotomizing between ROSC and No-ROSC might be different during CPR. In a canine model (n=18) of prolonged ventricular fibrillation (VF), standard CPR was provided with administration of two doses of epinephrine 0 min or 3 min later of the onset of CPR. For the analysis of the likelihood of a successful defibrillation during CPR, Support Vector Classification was adopted to evaluate statistical peculiarity combining time and frequency based predictors: median frequency, frequency band-limited power spectrum, mean segment amplitude, and zero crossing rates. The worst predictable period showed below about 1 min after the onset of CPR, and the best predictable period could be observed from about 1.5 min later of the administering epinephrine through 2.0-2.2 min. As hypothesized, the discrepancy of statistical characteristics of the predictors was reflected in the differences of the classification performance during CPR. These results represent a major improvement in defibrillation prediction can be achieved by a specific timing of the analysis, as a change in CPR transition.

Keywords

References

  1. Weaver WD, Cobb LA, Dennis D, et al., 'Amplitude of ventricular fibrillation waveform and outcome after cardiac arrest,' Ann. Intern. Med., vol. 102, no. 12, pp.53-55, 1985 https://doi.org/10.7326/0003-4819-102-1-53
  2. Brown CG, Dzwonczyk R, 'Signal analysis of human electrocardiogram during ventricular fibrillation: frequency and amplitude parameters as predictors of successful shock,' Ann. Emerg. Med., vol. 17, no. 14, pp.426 - 437, 1996
  3. Strohmenger HU, Lindner KH, Brown CG, 'Analysis of the ventricular fibrillation ECG signal amplitude and frequency parameters as predictors of countershock success in humans,' Chest, vol. 111, no. 8, pp.584-589, 1997 https://doi.org/10.1378/chest.111.3.584
  4. Callaway CW, Sherman LD, Mosesso VN, et al., 'Scaling exponent predicts defibrillation success for out-of-hospital cardiac arrest,' Circulation, vol. 103, no. 13, pp.1656-1661, 2001 https://doi.org/10.1161/01.CIR.103.12.1656
  5. Podbregar M, Kovaib M, Podbregar-Marc A, et al., 'Predicting defibrillation success by 'genetic' programming in patients with out-of-hospital cardiac arrest,' Resuscitation, vol. 57, no. 2, pp.153-159, 2003 https://doi.org/10.1016/S0300-9572(03)00030-3
  6. Jekova I, Mougeolle F, Valance A, 'Defibrillation shock success estimation by a set of six parameters derived from the electrocardiogram,' Physiol. Meas., vol. 25, no. 19, pp.1179 -1188, 2004 https://doi.org/10.1088/0967-3334/25/5/008
  7. Eisenberg M, Bergner L, Hallstrom A, 'Paramedic programs and out-of-hospital cardiac arrest: I. Factors associated with successful resuscitation,' Am. J. Public Health, vol. 69, no. 1, pp.30-38, 1979 https://doi.org/10.2105/AJPH.69.1.30
  8. Cobb LA, Fahrenbruch CE, Walsh TR, Copass MK, Olsufka M, Breskin M, and Hallstrom AP, 'Influence of cardiopulmonary resuscitation prior to defibrillation in patients with out-of-hospital ventricular fibrillation,' JAMA, vol. 281, no. 13, pp.1182-1188, 1999 https://doi.org/10.1001/jama.281.13.1182
  9. WikL, Hansen TB, Fylling F, Steen T, Vaagenes P, Auestad BH, and Steen PA, 'Delaying defibrillation to give basic cardiopulmonary resuscitation to patients with out-of-hospital ventricular fibrillation: a randomized trial,' JAMA, vol. 289, no. 11, pp. 1389-1395, 2003 https://doi.org/10.1001/jama.289.11.1389
  10. American Heart Association, 'Guidelines for cardiopulmonary resuscitation and emergency cardiovascular care,' Circulation, vol. 102, no. 8, pp. 1343-1357, 2000
  11. Chandra N, Rudikoff M, and Weisfeldt ML, 'Simultaneous chest compression and ventilation at high airway pressure during cardiopulmonary resuscitation,' Lancet., vol. 26, no. 1, pp.175-178, 1980
  12. Ralston SH, Voorhees WD, and Babbs CF, 'Intrapulmonary epinephrine during prolonged cardiopulmonary resuscitation: improved regional blood flow and resuscitation in dogs,' Ann. Emerg. Med., vol. 13, no. 2, pp.79-86, 1984 https://doi.org/10.1016/S0196-0644(84)80566-1
  13. Halperin HR, Tsitlik JE, Guerci AD, Mellits ED, Levin HR, Shi AY, Chandra N, and Weisfeldt ML, 'Determinants of blood flow to vital organs during cardiopulmonary resuscitation in dogs,' Circulation, vol. 73, no. 3, pp.539-550, 1986 https://doi.org/10.1161/01.CIR.73.3.539
  14. Sung Oh Hwang, Kang Hyun Lee, Jun Hwi Cho, Bum Jin Oh, 'Simulatenous stemothoracic cardiopulmonary resuscitation: A new method of cardiopulmonary resuscitation,' Resuscitation, vol. 48, no. 9, pp.293-299, 2001 https://doi.org/10.1016/S0300-9572(00)00250-1
  15. Monson H.H., Statistical Digital Signal Processing and Modeling, New York, John Wiley and sons, 1996, pp. 415-420
  16. Vapnik V.N., Statistical Learning Theory, New York, John Wiley and Sons, 1998, pp. 145-154
  17. Vapnik V.N., Kotz S., Estimation of Dependences Based on Empirical Data, New York, Springer, 2006, pp. 139-161
  18. Iwi G, Millard RK, Palmer AM, et al., 'Bootstrap resampling: a powerful method of assessing confidence intervals for doses from experimental data,' Phys. Med. Biol., vol. 44, no. 4, pp. 55-62, 1999 https://doi.org/10.1088/0031-9155/44/4/021
  19. Strohmenger HU, Lindner KH, Prengel AW, et al., 'Effects of epinephrine and vasopressin on median fibrillation frequency and defibrillation success in a porcine model of cardiopulmonary resuscitation,' Resuscitation, vol. 31, no. 17, pp.65-73, 1996 https://doi.org/10.1016/0300-9572(95)00899-3
  20. S.H. Lee, S.O. Hwang, Y.R. Yoon, 'Detection of Ventricular Fibrillation using Time-Frequency Analysis,' J. of Biomed. Eng. Res., vol. 20, no. 6, pp.567-571, 1999