Performance metrics for the accurate characterisation of interictal spike detection algorithms

Alexander J. Casson, Elena Luna, Esther Rodriguez-Villegas

    Research output: Contribution to journalArticlepeer-review


    Automated spike detection methods for the epileptic EEG are highly desired to speed up and disambiguate EEG analysis. However, it is difficult to accurately and concisely present the performance of such algorithms due to the large number of recording and algorithm variables that must be accounted for. This paper summarizes the core variables involved and presents different methods for calculating the average performance. These methods incorporate weighting factors to correct for non-ideal test cases. The factors are found to have a significant effect on the appearance of the results and the performance level that the algorithm appears to achieve. Four different weighting factors are considered and a duration divided by the number of events weighting is recommended for use in future studies. © 2008 Elsevier B.V. All rights reserved.
    Original languageEnglish
    Pages (from-to)479-487
    Number of pages8
    JournalJournal of Neuroscience Methods
    Issue number2
    Publication statusPublished - 15 Mar 2009


    • Averaging results
    • Interictal scalp EEG
    • Performance metrics
    • Sensitivity
    • Spike detection


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