A pivotal component in AEDs is the detection of ventricular fibrillation 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 real time 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 one second without any preselection. We used the BIH-MIT arrhythmia, the CU, and the AHA database. For a new ventricular fibrillation detection algorithm we calculated the sensitivity, specificity, and the area under its receiver operating characteristic curve (ROC) and compared these values with the results from an earlier investigation of several different ventricular fibrillation detection algorithms. This new algorithm is based on the Hilbert transform and outperforms all other investigated algorithms.
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