Fast statistical model-based classification of epileptic EEG signals

Abstract

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 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 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 Review of EEG Signal Analysis for Diagnosis of Neurological Disorders using Machine Learning

“…In Ref. [28], the authors use statistical models for classification, which allows seizure detection simultaneously in the different brain rhythms, complying with current medical practices. However, it has some limitations wherein firstly it is difficult to define the sliding time window and the overlap between epochs because of the very high dynamics of epileptic signals.…”

“…In the same way random forest, back proposition, generalized relevance learning vector quantization (GRlVQ), WT with random forest, WT with SVM, fuzzy logic and, wavelet-bank filtering and dimensionality reduction using a generalized Gaussian distribution (GGD) with LDA [23,28] are also used to classify epileptic signals. Focal epilepsy is a variant of epilepsy that mostly occurs in the limited area of the brain.…”

Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals

“…This statistical modeling stage gives M × [N/60] pairs of scale (ς) and shape (τ) parameters, achieving a very strong dimension reduction. As we demonstrate it in the experimentation section, the scale parameter ς was found statistically characteristic of the SWD waveform, and it is proposed as a feature to detect such patterns [4,44]. Using a single parameter to classify patterns is too strict and misses the natural variability in the data.…”

“…In the third stage, the statistical distribution of the wavelet coefficients from each segment is represented using a zero-mean generalized Gaussian distribution (GGD). A maximum likelihood method is used to estimate the GGD parameters, scale (ς) and shape (τ) [4,[42][43][44]. This statistical modeling stage gives M × [N/60] pairs of scale (ς) and shape (τ) parameters, achieving a very strong dimension reduction.…”

“…For more details about the estimation of the GGD parameters, we refer the reader to our previous work [4,[42][43][44]48].…”