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access icon openaccess Discriminant feature level fusion based learning for automatic staging of EEG signals

This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)
Received 19/05/2018, Accepted 10/07/2018, Revised 02/07/2018, Published 31/08/2018

Wide-scale information embedding is a prerequisite to enhance the performance as well as the reliability of decision-making algorithms for viable implementation. Feature fusion technology significantly helps to incorporate such information to provide promising algorithm performance. In this Letter, a fusion-based model with the aid of discriminant correlation analysis to classify electroencephalogram signals is proposed. Sets of multiple feature matrices are generated from signals in both time and wavelet domains for study-specific classes, which are further decomposed to derive a set of sub-multi-view features followed by optimisation to extract statistical features. Features are concatenated using feature fusion technique to derive low order discriminant features. Besides, the analysis of variance was also performed to validate the analysis. The statistically significant features are evaluated for the effective model performance. Experimental results manifest that the proposed feature fusion based algorithm is superior to many state-of-the-art methods and thus promote real-time implementation.

Inspec keywords: feature extraction; statistical analysis; image fusion; medical signal processing; matrix algebra; electroencephalography; learning (artificial intelligence); wavelet transforms

Other keywords: promising algorithm performance; statistical features; wavelet domains; automatic staging; multiple feature matrices; feature fusion based algorithm; viable implementation; effective model performance; wide-scale information embedding; electroencephalogram signals; EEG signals; feature fusion technology; real-time implementation; discriminant correlation analysis; fusion-based model; study-specific classes; decision-making algorithms; discriminant feature level fusion based learning; feature fusion technique; sub-multiview features; statistically significant features; low order discriminant features

Subjects: Computer vision and image processing techniques; Knowledge engineering techniques; Other topics in statistics

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