Improved Spike Detection Algorithm Based on Multi-Template Matching and Feature Extraction

Spike detection is of great significance in the detection of epileptic seizures. Many spike detection algorithms have been proposed, but available algorithms often miss spikes during high firing rate epochs or with artifacts. Also, there exists methodological limitations when there are great variations in spike morphology between different patients or the same patient in different EEG epoch. Thus a data-driven spike detection method using multi-template matching, feature extraction and threshold method is proposed in this work to improve the detection performance under different circumstances. First, universal template matching and feature extraction are used to get putative spikes. Then clustering algorithm and adaptive template matching algorithm are applied to maximize the number of detected spikes for these specific candidate single units obtained from the clustering method. In addition, a data-driven approach based on receiver operating characteristic (ROC) is used for low false identification rate detection. The performance is evaluated on real EEG samples and the evaluating results shows that this algorithm has achieved 97.12% average sensitivity and 0.55 average false negative rate per minute. The least sensitivity is 94.32% and the most false negative rate per minute is 1.23.