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Predicting defibrillation success with a multiple-domain model using machine learning

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4 Author(s)
Sharad Shandilya ; Department of Computer Science and VCURES, Virginia Commonwealth University, Richmond, 23220 USA ; Kevin R Ward ; Michael Kurz ; Kayvan Najarian

Ventricular Fibrillation(VF) waveform can represent rapidly worsening chances of defibrillation success, and those of subsequent Return of Spontaneous Circulation (ROSC), during a cardiac arrest. We propose a new method to analyze the chaotic nature of VF using multiple feature extraction and machine learning techniques. Human cardiac arrest data was acquired from the Richmond Ambulance Authority. A Multiple-Domain Model (MDM), which utilizes time-series and wavelet features, was developed. We report two new time-series features that are predictive of countershock (CS) success. Support vector machines were used with a radial basis function to classify 56 CS, 21 successful and 35 unsuccessful, with an average accuracy of 83.9%. Sensitivity and specificity were 71.4% and 91.4%, respectively. ROC area under the curve of 81.4% was achieved. The proposed predictive model performs real-time, short-term analysis of ECG, through signal-processing and machine-learning techniques, and can be accurate enough for clinical application. As more cardiac arrest data is acquired, improved MDM performance is anticipated.

Published in:

Complex Medical Engineering (CME), 2011 IEEE/ICME International Conference on

Date of Conference:

22-25 May 2011