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Fast statistical model-based classification of epileptic EEG signals

Quintero-Rincón A., Pereyra M., D'Giano C., Risk M., Batatia H.

Biocybernetics and Biomedical Engineering

2018

Vol. 38, no. 4

877--889

This 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 awavelet 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 straight-forward 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 exhibit healthy or abnormal brain activity in each spectral band. The good performance of the proposed method is demonstrated with an application to paediatric neurology using 39 EEG recordings from the Children's Hospital Boston database, where it achieves an average sensitivity of 98%, specificity of 88%, and detection latency of 4 s, performing similarly to the best approaches from the literature.

A wavelet-based model for foveal detection of spatial contrast with frequency dependent aperture effect

Hooshangnejad H., Gharibzadeh S., Towhidkhah F.

Advances in Science and Technology. Research Journal

2016

Vol. 10, nr 30

51--68

The main purpose of this study is to build a Computational model based on ModelFest dataset which is able to predict contrast sensitivity while it benefits from simplicity, efficiency and accuracy, which makes it suitable for hardware implementation, practical uses, online tests, real-time processes, an improved Standard Observer and retina prostheses. It encompasses several components, and in particular, frequency dependent aperture effect (FDAE) which is used for the first time on this dataset, which made the model more accurate and closer to reality. Shortcomings of previous models and the necessity of existence of FDAE for more accuracy led us to develop a new model based on Wavelet Transform that gives us the advantage of speed and the capability to process each frequency channels output. Considering our goal for building an efficient model, we introduce a new formula for modeling contrast sensitivity function, which generates fewer errors and better timing performance. Eventually, this new model leads to having as yet lowest RMS error and solving the problem of long execution time of prior models and reduces them by almost a factor of twenty.