Casson Alexander J, Luna Elena, Rodriguez-Villegas Esther
Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, United Kingdom.
J Neurosci Methods. 2009 Mar 15;177(2):479-87. doi: 10.1016/j.jneumeth.2008.10.010. Epub 2008 Oct 21.
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.
人们迫切需要用于癫痫脑电图（EEG）的自动尖峰检测方法，以加快EEG分析并消除歧义。然而，由于必须考虑大量的记录和算法变量，因此很难准确、简洁地呈现此类算法的性能。本文总结了所涉及的核心变量，并介绍了计算平均性能的不同方法。这些方法纳入了加权因子，以校正非理想测试案例。发现这些因子对结果的呈现以及算法似乎达到的性能水平有重大影响。考虑了四种不同的加权因子，并建议在未来研究中使用持续时间除以事件数的加权方法。
