Ventricular fibrillation detection algorithm for automated external defibrillators

The automatic external defibrillator (AED) is a lifesaving device, which processes and analyzes the electrocardiogram (ECG) and prompts a defibrillation shock if ventricular fibrillation is determined. This project investigates the possibility of developing a Ventricular Fibrillation (VF) detection algorithm based on Autoregressive Modeling (AR Modeling) and dominant poles for the use in AEDs. In particular, the ECG segment is modeled using AR modeling and the dominant poles are extracted from the model transfer function. The dominant pole frequencies were then used in classification based on the distance measure. The potential use of this method to distinguish between VF and Normal sinus rhythm (NSR) is discussed. The method was tested with ECG records from the widely recognized databases of American Heart Association (AHA) and the Creighton University (CU). Sensitivity and specificity for the new VF detection method were calculated to be 66% and 94% respectively. The proposed method has some advantages over other existing VF detection algorithms; it has a high detection accuracy, it is computationally inexpensive and can be easily implemented in hardware.