Brain EEG time series selection

a novel graph-based approach for classification

Chenglong Dai, Jia Wu, Dechang Pi, Lin Cui

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

2 Citations (Scopus)

Abstract

Brain Electroencephalography (EEG) classification is widely applied to analyze cerebral diseases in recent years. Unfortunately, invalid/noisy EEGs degrade the diagnosis performance and most previously developed methods ignore the necessity of EEG selection for classification. To this end, this paper proposes a novel maximum weight clique-based EEG selection approach, named mwcEEGs, to map EEG selection to searching maximum similarity-weighted cliques from an improved Fréchet distance-weighted undirected EEG graph simultaneously considering edge weights and vertex weights. Our mwcEEGs improves the classification performance by selecting intra-clique pairwise similar and inter-clique discriminative EEGs with similarity threshold δ. Experimental results demonstrate the algorithm effectiveness compared with the state-of the-art time series selection algorithms on real-world EEG datasets.

Original languageEnglish
Title of host publicationProceedings of the 2018 SIAM International Conference on Data Mining
EditorsMartin Ester, Dino Pedreschi
Place of PublicationPhiladelphia
PublisherSociety for Industrial and Applied Mathematics Publications
Pages558-566
Number of pages9
ISBN (Electronic)9781611975321
Publication statusPublished - 1 Jan 2018
Event2018 SIAM International Conference on Data Mining, SDM 2018 - San Diego, United States
Duration: 3 May 20185 May 2018

Conference

Conference2018 SIAM International Conference on Data Mining, SDM 2018
CountryUnited States
CitySan Diego
Period3/05/185/05/18

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