Fast statistical model-based classification of epileptic EEG signals

Publication date: Available online 21 August 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Antonio Quintero-Rincón, Marcelo Pereyra, Carlos D’Giano, Marcelo Risk, Hadj BatatiaAbstractThis paper presents a supervised classification method to accurately detect epileptic brain activity in real-time from electroencephalography (EEG) data. The proposed method has three main strengths: it has low computational cost, making it suitable for real-time implementation in EEG devices; it performs detection separately for each brain rhythm or EEG spectral band, following the current medical practices; and it can be trained with small datasets, which is key in clinical problems where there is limited annotated data available. This is in sharp contrast with modern approaches based on machine learning techniques, which achieve very high sensitivity and specificity but require large training sets with expert annotations that may not be available. The proposed method proceeds by first separating EEG signals into their five brain rhythms by using a wavelet filter bank. Each brain rhythm signal is then mapped to a low-dimensional manifold by using a generalized Gaussian statistical model; this dimensionality reduction step is computationally straightforward and greatly improves supervised classification performance in problems with little training data available. Finally, this is followed by parallel linear classifications on the statistical manifold to detect if the signals...
Source: Biocybernetics and Biomedical Engineering - Category: Biomedical Engineering Source Type: research