A new ventricular fibrillation detection algorithm based on time-delay methods outperformed all other investigated algorithms in terms of sensitivity, specificity, and area under the ROC curve.
A pivotal component in automated external defibrillators (AEDs) is the detection of ventricular fibrillation (VF) by means of appropriate detection algorithms. In scientific literature there exists a wide variety of methods and ideas for handling this task. These algorithms should have a high detection quality, be easily implementable, and work in realtime in an AED. Testing of these algorithms should be done by using a large amount of annotated data under equal conditions. For our investigation we simulated a continuous analysis by selecting the data in steps of 1 s without any preselection. We used the complete BIH-MIT arrhythmia database, the CU database, and files 7001-8210 of the AHA database. For a new VF detection algorithm we calculated the sensitivity, specificity, and the area under its receiver operating characteristic curve and compared these values with the results from an earlier investigation of several VF detection algorithms. This new algorithm is based on time-delay methods and outperforms all other investigated algorithms.
Amann et al. (Tue,) conducted a other in Ventricular fibrillation. New VF detection algorithm based on time-delay methods vs. Several earlier VF detection algorithms was evaluated on Sensitivity, specificity, and area under the receiver operating characteristic curve for VF detection. A new ventricular fibrillation detection algorithm based on time-delay methods outperformed all other investigated algorithms in terms of sensitivity, specificity, and area under the ROC curve.