Anton Amann1, Robert Tratnig, Karl Unterkofler. 1. Department of Anesthesia and General Intensive Care, Innsbruck Medical University, Anichstr. 35, A-6020 Innsbruck, Austria. Anton.Amann@uibk.ac.at
Abstract
BACKGROUND: A pivotal component in automated external defibrillators (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. METHODS: For our investigation we simulated a continuous analysis by selecting the data in steps of one second without any preselection. We used the complete BIH-MIT arrhythmia database, the CU database, and the files 7001-8210 of the AHA database. All algorithms were tested under equal conditions. RESULTS: For 5 well-known standard and 5 new ventricular fibrillation detection algorithms we calculated the sensitivity, specificity, and the area under their receiver operating characteristic. In addition, two QRS detection algorithms were included. These results are based on approximately 330,000 decisions (per algorithm). CONCLUSION: Our values for sensitivity and specificity differ from earlier investigations since we used no preselection. The best algorithm is a new one, presented here for the first time.
BACKGROUND: A pivotal component in automated external defibrillators (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. METHODS: For our investigation we simulated a continuous analysis by selecting the data in steps of one second without any preselection. We used the complete BIH-MIT arrhythmia database, the CU database, and the files 7001-8210 of the AHA database. All algorithms were tested under equal conditions. RESULTS: For 5 well-known standard and 5 new ventricular fibrillation detection algorithms we calculated the sensitivity, specificity, and the area under their receiver operating characteristic. In addition, two QRS detection algorithms were included. These results are based on approximately 330,000 decisions (per algorithm). CONCLUSION: Our values for sensitivity and specificity differ from earlier investigations since we used no preselection. The best algorithm is a new one, presented here for the first time.
Authors: R E Kerber; L B Becker; J D Bourland; R O Cummins; A P Hallstrom; M B Michos; G Nichol; J P Ornato; W H Thies; R D White; B D Zuckerman Journal: Circulation Date: 1997-03-18 Impact factor: 29.690
Authors: Nicolle J Domnik; Sami Torbey; Geoffrey E J Seaborn; John T Fisher; Selim G Akl; Damian P Redfearn Journal: J Comp Physiol B Date: 2021-07-25 Impact factor: 2.200