An Automatic Spike Detection System Based on Elimination of False Positives Using the Large-Area Context in the Scalp EEG

Most automatic spike detection systems in the scalp electroencephalogram (EEG) focused on the characteristics of “spike.” However, the characteristics of “false positives” (FPs) have not been fully studied. In this paper, we proposed a system that contains a series of algorithms to eliminate FPs and a template method to confirm spikes. The system used large area context available on 49 channels from two common montages. The impact of slow-waves after spikes was taken into consideration, as well as the information from single channel, multichannel, and whole recording. Two types of FPs were identified in this paper. The ones from typical artifacts were identified by analysis of background EEG activities, and the ones from other EEG activities were declared by spatial and temporal characteristics of spike activities. Finally, a multichannel template method was used to assess the performance of the proposed system. The system was evaluated using 17 routine EEG recordings. Spike activities were observed in six of them. Effective multichannel templates were extracted from four recordings containing frequent spikes. The least selectivity was 92.6% and the most false positive rate was 0.26 min^-1. Proposed algorithms for elimination of FPs are also suitable for other algorithms to enhance performance since most FPs can be identified while few true spikes are eliminated.