Detecting ventricular fibrillation by time-delay methods.
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
Anton
A
Department of Anesthesia and General Intensive Care, Innsbruck Medical University, A-6020 Innsbruck, Austria. Anton.Amann@uibk.ac.at
Tratnig
Robert
R
Unterkofler
Karl
K
D003198:Computer Simulation; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D008955:Models, Cardiovascular; D012815:Signal Processing, Computer-Assisted; D012984:Software; D012986:Software Validation; D013673:Technology Assessment, Biomedical / Q000379:methods; D014693:Ventricular Fibrillation / Q000175:diagnosis*