Abstract
Automatic detection of epileptic spikes is an important clinical application. It has been developed nearly 40 years. Yet the current automatic detection results are still not as reliable as experienced human interpreters, mainly due to the complex morphology of spikes and the similarity between paroxysmal events in brain activities. By reviewing the previous work, it is noticeable that the implementation of wavelet, ANN and spatiotemporal analysis show promising prospect. By reasonable combination of detection strategies, the detection rate can reach 90% with acceptable false detection rate. Current researches also reveal the reality of lacking of uniform dataset and rules for algorithm comparison. This review aims to compare the pros and cons of current researches and discuss the trend of development in the future.
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