A Random Tree machine learning model analyzing 40-second ECG tracings predicted successful defibrillation with an average accuracy of 71%.
Can machine learning analysis of time-series features from 40-second ECG tracings predict the success of defibrillation in ventricular fibrillation?
Real-time analysis of 40-second ECG tracings using machine learning can predict the success of defibrillation with 71% accuracy.
Chances of successful defibrillation, and that of subsequent return of spontaneous circulation (ROSC), worsen rapidly with passage of time during cardiac arrest. The Electrocardiogram (ECG) signal of ventricular fibrillation (VF) has been analyzed for certain characteristics which may be predictive of successful defibrillation. Time-series features were extracted. A total of 59 counter shocks (CS) were analyzed. They were best classified as successful or unsuccessful by employing the Random Tree method. An average accuracy of 71% was achieved for 6 randomized runs of 6-fold cross validation. Classification could be performed on ECG tracings of 40 seconds. Real-time, short-term analysis of ECG, through signal-processing and machine-learning techniques, may be valuable in determining CS success.
Shandilya et al. (Wed,) conducted a other in Cardiac arrest with ventricular fibrillation (n=59). Random Tree machine learning model on ECG time-series features was evaluated on Successful vs unsuccessful defibrillation classification accuracy. A Random Tree machine learning model analyzing 40-second ECG tracings predicted successful defibrillation with an average accuracy of 71%.
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