The Transactions of the Korean Institute of Electrical Engineers D (대한전기학회논문지:시스템및제어부문D)

The Korean Institute of Electrical Engineers (대한전기학회)
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A Prediction of Coronary Perfusion Pressure Using the Extracted Parameter From Ventricular Fibrillation ECG Wave

심실세동 심전도 파형 추출 파라미터를 이용한 관상동맥 관류압 예측

  • 장승진 (연세대학교 보건과학대학 의공학부) ;
  • 황성오 (연세대학교 원주의과대학 응급의학교실) ;
  • 윤영로 (연세대학교 보건과학대학 의공학부) ;
  • 이현숙 (상지대학교 이공과대학 컴퓨터전자물리학과)
  • Published : 2005.04.01
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Abstract

Coronary Perfusion Pressure(CPP) is known for the most important parameter related to the Return of Spontaneous Circulation (ROSC), however, clinically measuring CPP is difficult either invasive or non-invaisive method. En this paper, we analyze the correlation between the extracted parameter from VF ECG wave and the CPP with the statistical method, and predict CPP value using the extracted parameters within significance level. the extracted parameters are median frequency(MF), peak frequency(PF), average segment amplitude(ASA), MSA(maximum segment amplitude), Two parameters, MF, and ASA are selected in order to predict CPP value with general regression neural network, and then we evaluated the agreement statistics between the simulated CPP and the measured CPP. In conclusion, the mean and variance of the difference between the simulated CPP and the measured CPP are 8.9716±1.3526 mmHg, and standard deviation 6.4815 mmHg with one hundred-times training and test results. the simulated CPP and the measured CPP are agreed with the overall accuracy $90.68\%$ and kappa coefficient $81.14\%$ as a discriminant parameter of ROSC.

Keywords

References

  1. Vander & Sherman & Luciano., 'Human Physiology', McGraw Hill, pp. 449-450, 2001
  2. Jose Jalife, 'Ventricular Fibrillation: Mechanisms of Initiation and Maintenance', Annu. Rev. Physiol. 62:25-50 2000 https://doi.org/10.1146/annurev.physiol.62.1.25
  3. Strohmenger, Hans-Ulrich MD; Lindner, Karl H. MD; Brown, Charles G. MD, 'Analysis of the Ventricular Fibrillation ECG Signal Amplitude and Frequency Parameters as Predictors of Countershock Success in Humans', Cardiopulmonary and Critical Care Journal, volume 111(3), pp 584-589, March 1997 https://doi.org/10.1378/chest.111.3.584
  4. Ahmet Baykal, Ravi Ranjan, Nitish V. Thakor, 'Estimation of the Ventricular Fibrillation Duration by Autoregressive Modeling', IEEE transactions on Biomedical Engineering, Vol. 44, No. 5, May 1997 https://doi.org/10.1109/10.568910
  5. M Small, DJ Yu, RG Harrison, C Robertson, G Clegg, M Holzer, F Sterz, 'Characterizing Nonlinearity In Ventricular Fibrillation', IEEE Computers in Cardiology, 26:17-20, 1999 https://doi.org/10.1109/CIC.1999.825895
  6. Brown, Charles G MD*; Dzwonczyk, Roger MSBME, 'Signal Analysis of the Human Electrocardiogram During Ventricular Fibrillation: Frequency and Amplitude Parameters as Predictors of Successful Countershock', Annals of Emergency Medicine, Mosby-Year Book Inc., volume 27(2), pp 184-188, February 1996 https://doi.org/10.1016/S0196-0644(96)70346-3
  7. DE Ritscher, CR Killingsworth, GP Walcott, RE Ideker, WM Smith, 'Ventricular Fibrillation Frequency Analysis: Signatures of Models of Death in a Canine Sudden Cardiac Death Model', IEEE Computers in Cardiology, 26:623-626, 1999 https://doi.org/10.1109/CIC.1999.826048
  8. Sung Oh Hwang, Kang Hyun Lee, Jun Hwi Cho, Bum Jin Oh., 'Simultaneous sternothoracic cardiopulmonary resuscitation: A new method of cardiopulmonary resuscitation', Elsevier Science Ireland Ltd., Resuscitation 48, pp 293-299, 2001 https://doi.org/10.1016/S0300-9572(00)00250-1
  9. DJ Yu et al, 'Complexity Measurements for Analysis and Diagnosis of Early Ventricular Fibrillation', IEEE Computers in Cardiology, 26:21-24, 1999 https://doi.org/10.1109/CIC.1999.825896
  10. Redding JS & Pearson JW., 'Evaluation of drugs for cardiac resuscitation', Anesthesiology Vol. 24, pp.203-207, 1963 https://doi.org/10.1097/00000542-196303000-00008
  11. J.P. Tournadre et al, 'Overestimation of low cardiac output measured by thermodilution', Brith Journal of Anaesthesia, 79:541-516, 1997
  12. Karl B. 'Coronary perfusion pressure during cardiopulmonary resuscitation', Bailliere's Clinical Anaesthesiology vol. 14, No. 3, pp.591-609, 2000 https://doi.org/10.1053/bean.2000.0109
  13. Monson H. Hayes., 'Statistical Digital Signal Processing And Modeling', John Wiley & Sons, Inc., pp.415-420, 1996
  14. Trygve Eftestol, Sivlng., 'Predicting Outcome of Defibrillation by Spectral Characterization and Nonparametric Classification of Ventricular Fibrillation in Patients with Out-of-Hospital Cardiac Arrest', Circulation, September, 26, 2000
  15. Yamakawa, A.; Honda, K.; Ichihashi, H.; Miyoshi, T. 'Simultaneous approach to fuzzy cluster, principal component and multiple regress-tion analysis', Neural Networks, 1999. IJCNN '99. International Joint Conference, Volume: 6 , 1999 https://doi.org/10.1109/IJCNN.1999.830864
  16. J Guerrero, JF Chorro, A Rosado, R Magdalena, E Soria, M Bataller, V Lopez-Merino, J. Espi, 'Spectral Coherence of Ventricular Fibri-llation under Conditions of Coronary Perfusion, Dillation and Drug Administration', IEEE Computers in Cardiology, 26:339-342, 1999 https://doi.org/10.1109/CIC.1999.825976
  17. Rosenberg, Jack M. MD; Wahr, Joyce A. MD; Sung, Ho Choon MD, PhD; Oh, Young Suk; Gilligan, Lori J. LVT, 'Coronary Perfusion Pressure During Cardiopul­monary Resuscitation After Spinal Anest-hesia in Dogs', Anesthesia & Analgesia, volume 82(1), pp 84-87, January 1996 https://doi.org/10.1097/00000539-199601000-00014
  18. Noc, Marko MD; Weil, Max Harry MD, PhD, FCCM; Tang, Wanchun MD, FCCM; Sun, Shijie MD: Pernat, Andrej MD; Bisera, Joe MSEE, 'Electrocardiographic prediction of the success of cardiac resuscitation', Critical Care Medicine, volume 27(4), pp 708-714, April 1999 https://doi.org/10.1097/00003246-199904000-00021