Detection of epileptic seizure employing a novel set of features extracted from multifractal spectrum of electroencephalogram signals

Abstract

Here, a technique for automated detection of epilepsy is proposed, based on a novel set of features derived from the multifractal spectrum of electroencephalogram (EEG) signals. In fractal geometry, multifractal detrended fluctuation analysis (MDFA) is a technique to examine the self-similarity of a non-linear, chaotic and noisy time series. EEG signals which are representatives of complex human brain dynamics can be effectively characterised by MDFA. Here, EEG signals representing healthy, interictal and seizure activities are acquired from an available dataset. The acquired signals are at first analysed using MDFA. Based on the multifractal analysis, 14 novel features are proposed in this study, to distinguish between different types of EEG signals. The statistical significance of the selected features is evaluated using Kruskal-Wallis test and is finally served as input feature vector to a support vector machines classifier for the classification of EEG signals. Four classification problems are presented in this work and it is observed that 100% classification accuracy is obtained for three problems which validate the efficacy of the proposed model for computer-aided diagnosis of epilepsy.

Detection of focal electroencephalogram signals using higher‐order moments in EMD‐TKEO domain

“…Among different kernel functions of SVM classifier, the RBF kernel function has been found to deliver better result and hence the performance of the SVM classifier has been evaluated in this study using RBF kernel functions. Mathematically, RBF kernel can be expressed as [22] where ( f , g ) is a linearly separable training data, and is the parameter of the RBF kernel and is the width. The choice of optimum width is particularly important since the performance of the SVM classifier depends on it.…”

“…Among different kernel functions of SVM classifier, the RBF kernel function has been found to deliver better result and hence the performance of the SVM classifier has been evaluated in this study using RBF kernel functions. Mathematically, RBF kernel can be expressed as [22] RBF = e (−g f −g 2 )…”

Feature extraction from multifractal spectrum of electromyograms for diagnosis of neuromuscular disorders

“…In the case of multifractal signals, (q) shows non-linear dependency on 'q' and therefore multifractal signals are characterised by multiple Hurst exponents. Applying Legendre transform, the singularity spectrum f(α) is related to the scaling exponent (q) using the following equation [23]:…”

“…A nonstationary signal can be therefore split into different segments and their scaling behaviour can be studied using different fluctuation functions. Application of the MDFA algorithm to study the dynamic fluctuations of several non-linear and non-stationary signals like partial discharge signals [21], accelerometer signals [22], epileptic seizure EEG signals [23], focal epileptic EEG signals [24] etc. were reported in the available literatures.…”

Multi-Chaotic Analysis of Inter-Beat (R-R) Intervals in Cardiac Signals for Discrimination between Normal and Pathological Classes

“…These interactions reflect the influence of numerous vital processes. For example, recent studies have shown that many biomedical signals have a multifractal structure [ 12 , 13 , 14 , 15 ]. Such signals represent a complex fractal structure, which cannot be sufficiently characterized by a single summary value (e.g., the Hurst exponent).…”