Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks

Abstract

The electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a time-consuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy have emerged in recent years. This paper proposes a neural-network-based automated epileptic EEG detection system that uses approximate entropy (ApEn) as the input feature. ApEn is a statistical parameter that measures the predictability of the current amplitude values of a physiological signal based on its previous amplitude values. It is known that the value of the ApEn drops sharply during an epileptic seizure and this fact is used in the proposed system. Two different types of neural networks, namely, Elman and probabilistic neural networks, are considered in this paper. ApEn is used for the first time in the proposed system for the detection of epilepsy using neural networks. It is shown that the overall accuracy values as high as 100% can be achieved by using the proposed system.

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

“…They achieved classification accuracy of 98.40% in classifying seizure, seizure-free and the normal classes of EEG signals. The classification of epileptic seizure EEG signals has been carried out by several other nonlinear features, namely recurrence quantification analysis (RQA) [11], Hurst exponent [12] and approximate entropy (ApEn) [13]. Local binary pattern (LBP)-based methods have been suggested in recent studies for the classification of epileptic seizure EEG signals.…”

Automated Epileptic Seizure Detection Methods: A Review Study

“…In addition, time domain features such as statistical features (Adjouadi et al, 2005), Hjorth's descriptors (Hjorth, 1970), nonlinear features (Kannathal, Acharya, Lim, & Sadasivan, 2005;McSharry, et al, 2002)-correlation dimension (Elger & Lehnertz, 1998), Lyapunov exponent Ubeyli, 2006;Ubeyli, 2010b) and other features obtained from convolution kernels (Adjouadi et al, 2004), eigenvector methods (Naghsh-Nilchi & Aghashahi, 2010 ; Ubeyli, 2008aUbeyli, , 2008bUbeyli, , 2009a, principal component analysis (PCA) (Ghosh-Dastidar, Adeli, & Dadmehr, 2008;Hesse & James, 2007;James & Hesse, 2005;Polat & Gunes, 2008a;Subasi & Gursoy, 2010), ICA (Hesse & James, 2007;James & Hesse, 2005;Subasi & Gursoy, 2010), crosscorrelation function (Chandaka, Chatterjee, & Munshi, 2009;Iscan, et al, 2011), and entropy (Guo, Rivero, Dorado, et al, 2010;Kannathal, Choo, Acharya, & Sadasivan, 2005;Liang, Wang, & Chang, 2010;Naghsh-Nilchi & Aghashahi, 2010 ;H. Ocak, 2009;Srinivasan, Eswaran, & Sriraam, 2007;Wang, et al, 2011) have been proposed to characterize the EEG signal. It is also possible to select features using genetic programming (Firpi, Goodman, & Echauz, 2005;Guo, Rivero, Dorado, Munteanu, & Pazos, 2011).…”

“…Just as a wide variety of features has been used, an equally varied set of classification methods can be found in the literature. The classification methods varied from simple threshold (Altunay, Telatar, & Erogul, 2010;Martinez-Vargas, et al, 2011), rule based decisions (Gotman, 1990(Gotman, , 1999, or linear classifiers (Ghosh-Dastidar, Iscan, et al, 2011;Liang, et al, 2010;Subasi & Gursoy, 2010) to ANNs , 2008Mousavi, et al, 2008;Nigam & Graupe, 2004;Srinivasan, et al, 2005Srinivasan, et al, , 2007Tzallas, et al, 2007aTzallas, et al, , 2007bTzallas, et al, , 2009Ubeyli, 2006Ubeyli, , 2009cUbeyli, 2010b) that have a complex shaped decision boundary. Other classification methods have been used using SVMs (Chandaka, et al, 2009;Iscan, et al, 2011;Liang, et al, 2010;Lima, et al, 2010;Subasi & Gursoy, 2010;Ubeyli, 2008a;Ubeyli, 2010a), k-nearest neighbour classifiers (Guo, et al, 2011;Iscan, et al, 2011;Liang, et al, 2010;Orhan, et al, 2011;Tzallas, et al, 2009), quadratic analysis (Iscan, et al, 2011), logistic regression Tzallas, et al, 2009), naive Bayes classifiers (Iscan, et al, 2011;Tzallas, et al, 2009), decision trees (Iscan, et al, 2011;Polat & Gunes, 2007;…”

Classification of Eeg Signals for Detection of Epileptic Seizures Based on Wavelets and Statistical Pattern Recognition

“…Srinivasan et al. [20] presented an algorithm based on approximate entropy as an input to an artificial neural network classifier, while Subasi [21] used wavelet analysis and mixture of experts, in addition to the artificial neural network, to classify EEG signals and detect seizures.…”

“…In addition, due to a lack of reliable standardized data, most reported EEG analysis-based algorithms are performed on a small number of datasets, which often demonstrate good accuracy for selected EEG segments but are not robust enough to adjust to EEG variations commonly encountered in a hospital setting [20]. In this research, however, a larger number of EEG data sets, which belong to three subject groups, were used: a) healthy subjects (normal EEG), b) epileptic subjects during a seizure-free interval (interictal EEG), and c) epileptic subjects during a seizure (ictal EEG).…”