Epileptic State Segmentation with Temporal-Constrained Clustering

Abstract

Automatic seizure identification plays an important role in epilepsy evaluation. Most existing methods regard seizure identification as a classification problem and rely on labelled training set. However, labelling seizure onset is very expensive and seizure data for each individual is especially limited, classifier-based methods are usually impractical in use. Clustering methods could learn useful information from unlabelled data, while they may lead to unstable results given epileptic signals with high noises. In this paper, we propose to use Gaussian temporal-constrained k-medoids method for seizure state segmentation. Using temporal information, the noises could be effectively suppressed and robust clustering performance is achieved. Besides, a new criterion called signed total variation (STV) which describes temporal integrity and consistency is proposed for temporal-constrained clustering evaluation. Experimental results show that, compared with the existing methods, the k-medoids method with Gaussian temporal constraint achieves the best results on both F1-score and STV.