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Open Access Technical advance

Non-linear dynamical signal characterization for prediction of defibrillation success through machine learning

Sharad Shandilya1*, Kevin Ward2, Michael Kurz3 and Kayvan Najarian4

Author Affiliations

1 Department of Computer Science, Virginia Commonwealth University, VCU Reanimation Engineering Science Center, 1818 Providence Creek Cir, Richmond, VA, 23236, MI

2 University of Michigan, Michigan Center for Integrative Research in Critical Care, Ann Arbor, MI

3 Department of Emergency Medicine, Virginia Commonwealth University, VCU Reanimation Engineering Science Center, Richmond, USA

4 Department of Computer Science, Virginia Commonwealth University, VCU Reanimation Engineering Science Center, Richmond, USA

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BMC Medical Informatics and Decision Making 2012, 12:116  doi:10.1186/1472-6947-12-116

Published: 15 October 2012

Abstract

Background

Ventricular Fibrillation (VF) is a common presenting dysrhythmia in the setting of cardiac arrest whose main treatment is defibrillation through direct current countershock to achieve return of spontaneous circulation. However, often defibrillation is unsuccessful and may even lead to the transition of VF to more nefarious rhythms such as asystole or pulseless electrical activity. Multiple methods have been proposed for predicting defibrillation success based on examination of the VF waveform. To date, however, no analytical technique has been widely accepted. We developed a unique approach of computational VF waveform analysis, with and without addition of the signal of end-tidal carbon dioxide (PetCO2), using advanced machine learning algorithms. We compare these results with those obtained using the Amplitude Spectral Area (AMSA) technique.

Methods

A total of 90 pre-countershock ECG signals were analyzed form an accessible preshosptial cardiac arrest database. A unified predictive model, based on signal processing and machine learning, was developed with time-series and dual-tree complex wavelet transform features. Upon selection of correlated variables, a parametrically optimized support vector machine (SVM) model was trained for predicting outcomes on the test sets. Training and testing was performed with nested 10-fold cross validation and 6–10 features for each test fold.

Results

The integrative model performs real-time, short-term (7.8 second) analysis of the Electrocardiogram (ECG). For a total of 90 signals, 34 successful and 56 unsuccessful defibrillations were classified with an average Accuracy and Receiver Operator Characteristic (ROC) Area Under the Curve (AUC) of 82.2% and 85%, respectively. Incorporation of the end-tidal carbon dioxide signal boosted Accuracy and ROC AUC to 83.3% and 93.8%, respectively, for a smaller dataset containing 48 signals. VF analysis using AMSA resulted in accuracy and ROC AUC of 64.6% and 60.9%, respectively.

Conclusion

We report the development and first-use of a nontraditional non-linear method of analyzing the VF ECG signal, yielding high predictive accuracies of defibrillation success. Furthermore, incorporation of features from the PetCO2 signal noticeably increased model robustness. These predictive capabilities should further improve with the availability of a larger database.

Keywords:
Cardiac arrest; Resuscitation; Ventricular fibrillation; CPR; Defibrillation success; Shock outcome; Complex wavelet transform; Non-linear analysis; Time-series analysis; Signal decomposition; Feature selection