9 December 2015 Application of linear graph embedding as a dimensionality reduction technique and sparse representation classifier as a post classifier for the classification of epilepsy risk levels from EEG signals
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Proceedings Volume 9817, Seventh International Conference on Graphic and Image Processing (ICGIP 2015); 98171D (2015) https://doi.org/10.1117/12.2227989
Event: Seventh International Conference on Graphic and Image Processing, 2015, Singapore, Singapore
Abstract
The most common and frequently occurring neurological disorder is epilepsy and the main method useful for the diagnosis of epilepsy is electroencephalogram (EEG) signal analysis. Due to the length of EEG recordings, EEG signal analysis method is quite time-consuming when it is processed manually by an expert. This paper proposes the application of Linear Graph Embedding (LGE) concept as a dimensionality reduction technique for processing the epileptic encephalographic signals and then it is classified using Sparse Representation Classifiers (SRC). SRC is used to analyze the classification of epilepsy risk levels from EEG signals and the parameters such as Sensitivity, Specificity, Time Delay, Quality Value, Performance Index and Accuracy are analyzed.
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Sunil Kumar Prabhakar, Harikumar Rajaguru, "Application of linear graph embedding as a dimensionality reduction technique and sparse representation classifier as a post classifier for the classification of epilepsy risk levels from EEG signals", Proc. SPIE 9817, Seventh International Conference on Graphic and Image Processing (ICGIP 2015), 98171D (9 December 2015); doi: 10.1117/12.2227989; https://doi.org/10.1117/12.2227989
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