Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals

According to the World Health Organization (WHO) epilepsy affects approximately 45–50 million people. Electroencephalogram (EEG) records the neurological activity in the brain and it is used to identify epilepsy. Visual inspection of EEG signals is a time-consuming process and it may lead to human error. Feature extraction and classification are two main steps that are required to build an automated epilepsy detection framework. Feature extraction reduces the dimensions of the input signal by retaining informative features and the classifier assigns a proper class label to the extracted feature vector. Our aim is to present effective feature extraction techniques for automated epileptic EEG signal classification.  In this study, two effective feature extraction techniques (Local Neighbor Descriptive Pattern [LNDP] and One-dimensional Local Gradient Pattern [1D-LGP]) have been introduced to classify epileptic EEG signals. The classification between epileptic seizure and non-seizure signals is performed using different machine learning classifiers. The benchmark epilepsy EEG dataset provided by the University of Bonn is used in this research. The classification performance is evaluated using 10-fold cross validation. The classifiers used are the Nearest Neighbor (NN), Support Vector Machine (SVM), Decision Tree (DT) and Artificial Neural Network (ANN). The experiments have been repeated for 50 times.  LNDP and 1D-LGP feature extraction techniques with ANN classifier achieved the average classification accuracy of 99.82% and 99.80%, respectively, for the classification between normal and epileptic EEG signals. Eight different experimental cases were tested. The classification results were better than those of some existing methods.  This study suggests that LNDP and 1D-LGP could be effective feature extraction techniques for the classification of epileptic EEG signals. .  Epilepsy is a central nervous system disorder and it has been eported that approximately 45–50 million people suffer from this isorder . EEG captures the neurological activity inside the brain y placing electrodes on the scalp and helps in detection of epilepic seizure [2,3]. Epileptic seizure detection can be considered as classification problem where the task is to classify an input sigal either as an epileptic seizure signal or as a non-seizure signal. EG signals are usually recorded for long durations to carry out n analysis. Detection of epileptic seizure in these long duration EG signals requires expertise. In the absence of expert, particuarly in emergencies, seizure detection becomes a challenging task. herefore, providing a framework for automated epileptic seizure ∗  E-mail address: abegiitdhanbad@gmail.com (A.K. Jaiswal). ttp://dx.doi.org/10.1016/j.bspc.2017.01.005 746-8094/  A number of methods have been proposed in literature for the classification of epileptic EEG signals. The methods can be categorized into following domains of signal analysis: Time domain analysis: Some techniques in the field of epileptic EEG signal classification belong to this category. Techniques like linear prediction [4], Fractional linear prediction , Principal component analysis based radial basis function neural network [6], etc. have been proposed for epileptic seizure detection. Frequency domain analysis: With the assumption that the EEG signals are stationary signals, Polat and Gunes [7] introduced a hybrid framework based on frequency domain analysis with Fourier transform and decision tree. In the hybrid model, Fourier transform was used for feature extraction and decision tree was used for the classification. Srinivasan et al.  used features extracted from time domain and frequency domain for seizure detection in EEG signals. The extracted features were fed to the Elman neural network to detect epileptic seizure. 82 A.K. Jaiswal, H. Banka / Biomedical Signal Processing and Control 34 (2017) 81–92 0 50