Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators

Anton Amann, Robert Tratnig, Karl Unterkofler, Anton Amann, Robert Tratnig, Karl Unterkofler

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.

Figures

Figure 1
Figure 1
Receiver operating characteristic for the algorithm "complexity measure" described in the introduction, for a window length of 8 s. The calculated value for the area under the curve, IROC, is 0.87.
Figure 5
Figure 5
ROC curve for the algorithms ACF, STE, MEA, WVL1, LI, TOMP.
Figure 6
Figure 6
ROC curve for the algorithms TCI, VF, SPEC, CPLX, SCA.
Figure 2
Figure 2
Binary signal with 2 pulses in threshold crossing intervals algorithm.
Figure 3
Figure 3
A 8 second episode of SR rhythm is investigated with the standard exponential algorithm (STE). The exponential function intersects the signal 12 times.
Figure 4
Figure 4
A 8 second episode of SR rhythm is investigated with the modified exponential algorithm (MEA). The exponential function is lifted 7 times.
Figure 7
Figure 7
ECG signal with relative maxima (indicated by stars left) after applying step 1 of Signal Comparison Algorithm (SCA). This ECG episode is annotated as no VF, a.u. ... arbitrary units.
Figure 8
Figure 8
ECG signal with relative maxima left (indicated by stars) after applying step 2 of Signal Comparison Algorithm (SCA).
Figure 9
Figure 9
ECG signal with relative maxima (indicated by stars) after applying step 4 of Signal Comparison Algorithm (SCA).
Figure 10
Figure 10
ECG signal with relative maxima and VF reference signal after applying step 6 of Signal Comparison Algorithm (SCA).
Figure 11
Figure 11
ECG signal with relative maxima and SR reference signal after applying step 6 of Signal Comparison Algorithm (SCA).

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Source: PubMed

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