Global Epileptic Seizure Identification with Affinity Propagation Clustering Partition Mutual Information Using Cross Layer Fully Connected Neural Network
- 1Hubei Normal University, China
- 2Wuhan University, China
A long-standing issue in epilepsy research and practice is to classify synchronization patterns hidden in multivariate electroencephalography (EEG) routinely superimposed with intensive noise. It is essential to select a suitable feature extraction method to achieve high recognition performance. A typical example is to extract the mutual information (MI) between pairs of channels. The effectiveness of its calculation, which considers the differences between the sequence pairs to build reasonable partition, will improve the classification performance. On this basis, it is even more difficult to adaptively classify the synchronization patterns hidden in multivariate EEG data under the circumstance of insufficient a prior knowledge of domain dependency such as denoising, feature extraction on special patient, etc. To address these problems: (1) effectively calculate the mutual information matrix (synchronization pattern), and (2) accurately classify the seizure or non-seizure state, this study first measures the synchronization between channel pairs in terms of Affinity Propagation Clustering Partition Mutual Information (APCPMI) accurately. The global synchronization measurement is then obtained by organizing APCPMIs of all channel pairs to a correlation matrix. Finally, a cross layer fully connected net is designed to characterize the synchronization dynamics correlation matrices adaptively and identify the states of seizure or non-seizure automatically. Experiments are performed over the CHB-MIT scalp EEG dataset to evaluate the proposed approach. Seizure states can be identified with an accuracy, sensitivity and specificity of $[97.93\% \pm\ 0.0001\%]$, $[99.42\% \pm\ 0.05\%]$, and $[96.76\% \pm\ 0.08\%]$, respectively; the resulted performance is superior to those of most existing methods over the same dataset. Furthermore, the approach alleviates the necessary for strictly preprocessing (denoising, removing interferences and artefact) the EEG in terms of prior knowledge, which is widely used in existing approaches.
Keywords: Affinity propagation clustering, MI, EEG, Epilepsy, synchronization, Pattern Classification
Received: 15 Jan 2018;
Accepted: 13 Sep 2018.
Edited by:Jay Hegdé, Augusta University, United States
Reviewed by:Quanying Liu, California Institute of Technology, United States
Duan Li, University of Michigan, United States
Copyright: © 2018 Wang and Ke. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Hengjin Ke, Wuhan University, Wuhan, China, email@example.com