Abstract
The diagnosis and treatment of epilepsy is a significant direction for both machine learning and brain science. This paper newly proposes a fast enhanced exemplar-based clustering (FEEC) method for incomplete EEG signal. The algorithm first compresses the potential exemplar list and reduces the pairwise similarity matrix. By processing the most complete data in the first stage, FEEC then extends the few incomplete data into the exemplar list. A new compressed similarity matrix will be constructed and the scale of this matrix is greatly reduced. Finally, FEEC optimizes the new target function by the enhanced α-expansion move method. On the other hand, due to the pairwise relationship, FEEC also improves the generalization of this algorithm. In contrast to other exemplar-based models, the performance of the proposed clustering algorithm is comprehensively verified by the experiments on two datasets.
Highlights
Epilepsy is a common disease of nervous system, which is characterized by sudden brain dysfunction
As we know that brain activity is a nonlinear, unstable complex network system, EEG signals we usually get are complicated. at is to say, some EEG signals are complete while others may miss some features, namely, incomplete
The performance of the k-means model relies on the initialization of data, while the fcm model requires high interpretability. us, we focus on the exemplar-based clustering model [13] which is proposed by Frey in this paper. e exemplar-based clustering model has the advantages of automatically obtaining the cluster number, high efficiency, and not relying on the initialization of data
Summary
Epilepsy is a common disease of nervous system, which is characterized by sudden brain dysfunction. EEG signals have the characteristics of high dimension and stochasticity which limit the performance of most existing clustering models, such as k-means [11] and fuzzy c mean (fcm) [12]. Us, we focus on the exemplar-based clustering model [13] which is proposed by Frey in this paper. Based on the previous work about the recognition of epileptic signals, we propose a novel fast enhanced exemplar-based clustering (FEEC) model for incomplete EEG signals. Different from existing exemplar-based clustering models, FEEC compresses the exemplar list and reduces the pairwise similarity matrix, and FEEC optimizes the target model by the enhanced α-expansion move framework. (2) Along with most existing exemplar-based clustering models, FEEC is built on the pairwise similarity matrix of data.
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