Predicting defibrillation success by "genetic" programming in patients with out-of-hospital cardiac arrest

Matej Podbregar (Author), Miha Kovačič (Author), Aleksandra Podbregar-Marš (Author), Miran Brezočnik (Author)

Abstract

In some patients with ventricular fibrillation (VF) there may be a better chance of successful defibrillation after a period of chest compression and ventilation before the defibrillation attempt. It is therefore important to know whether a defibrillation attempt will be successful. The predictive powerof a model developed by "genetic" programming (GP) to predict defibrillation success was studied. Methods and Results: 203 defibrillations were administered in 47 patients with out-of-hospital cardiac arrest due to a cardiac cause. Maximal amplitude, a total energy of power spectral density, and the Hurst exponent of the VF electrocardiogram (ECG) signal were included in the model developed by GP. Positive and negative likelihood ratios of the model for testing data were 35.5 and 0.00, respectively. Using a model developed by GP on the complete database, 120 of the 124 unsuccessful defibrillations would have been avoided, whereas all of the 79 successful defibrillations would have been administered. Conclusion: The VF ECG contains information predictive of defibrillation success. The model developed by GP, including data from the time-domain, frequency-domain and nonlinear dynamics, could reduce the incidence of unsuccessful defibrillations.

Keywords

optimizacijske metode;evolucijske optimizacijske metode;genetski algoritmi;genetsko programiranje;defibrilacija;napoved zastoja srca;optimisation methods;evolutionary optimisation methods;genetic algorithms;genetic programming;defibrillation;cardiac arrest prediction;

Data

Language:	English
Year of publishing:	2003
Typology:	1.01 - Original Scientific Article
Organization:	UM FS - Faculty of Mechanical Engineering
UDC:	004.89:611.12
COBISS:	7969814
ISSN:	0300-9572
Views:	1382
Downloads:	80
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary keywords:	optimizacijske metode;evolucijske optimizacijske metode;genetski algoritmi;genetsko programiranje;defibrilacija;napoved zastoja srca;
URN:	URN:SI:UM:
Pages:	str. 153-159
Volume:	ǂVol. ǂ57
Issue:	ǂiss. ǂ2
Chronology:	2003
ID:	8718837