Published ventricular fibrillation detection algorithms demonstrate reduced diagnostic accuracy when applied to independent ECG datasets, highlighting the importance of external validation.
VF algorithms lose accuracy on external ECGs; supports mandatory independent validation before clinical use.
The introduction and widening application of automatic external defibrillators (AEDs) present very strong requirements for external ECG signal analysis. Highly accuratc discrimination between shockable and non-shockable rhythms is required, with sensitivity and specificity aimed to approach the maximum values of 100%. We undertook an assessment of the performance of five detection algorithms, selected from among several others for their good published results. Test signals were 71 8 s ECG episodes on sinus rhythm and 90 8 s episodes on ventricular fibrillation, which were taken from the well known ECG-signal databases of the American Heart Association (AHA) and the Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH-'cudb' and 'vfdb' files). The purpose of this study is to assess the accuracy of the algorithms with signals other than those used for their development. An expected reduction of the sensitivity and specificity was found. The results could be used for further assessment, e.g. of noise and artefact sensitivity, for comparison with newly developed algorithms, etc.
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Irena Jekova (2000) studied this question.
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