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Epilepsy, a neurological disorder, is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals, which are used to detect the presence of seizures, are non-linear and dynamic in nature. Visual inspection of the EEG signals for detection of normal, interictal, and ictal activities is a strenuous and time-consuming task due to the huge volumes of EEG segments that have to be studied. Therefore, non-linear methods are being widely used to study EEG signals for the automatic monitoring of epileptic activities. The aim of our work is to develop a Computer Aided Diagnostic (CAD) technique with minimal pre-processing steps that can classify all the three classes of EEG segments, namely normal, interictal, and ictal, using a small number of highly discriminating non-linear features in simple classifiers. To evaluate the technique, segments of normal, interictal, and ictal EEG segments (100 segments in each class) were used. Non-linear features based on the Higher Order Spectra (HOS), two entropies, namely the Approximation Entropy (ApEn) and the Sample Entropy (SampEn), and Fractal Dimension and Hurst Exponent were extracted from the segments. Significant features were selected using the ANOVA test. After evaluating the performance of six classifiers (Decision Tree, Fuzzy Sugeno Classifier, Gaussian Mixture Model, K-Nearest Neighbor, Support Vector Machine, and Radial Basis Probabilistic Neural Network) using a combination of the selected features, we found that using a set of all the selected six features in the Fuzzy classifier resulted in 99.7% classification accuracy. We have demonstrated that our technique is capable of achieving high accuracy using a small number of features that accurately capture the subtle differences in the three different types of EEG (normal, interictal, and ictal) segments. The technique can be easily written as a software application and used by medical professionals without any extensive training and cost. Such software can evolve into an automatic seizure monitoring application in the near future and can aid the doctors in providing better and timely care for the patients suffering from epilepsy.

Epilepsy is a chronic brain disorder which manifests as recurrent seizures. Electroencephalogram (EEG) signals are generally analyzed to study the characteristics of epileptic seizures. In this work, we propose a method for the automated classification of EEG signals into normal, interictal and ictal classes using Continuous Wavelet Transform (CWT), Higher Order Spectra (HOS) and textures. First the CWT plot was obtained for the EEG signals and then the HOS and texture features were extracted from these plots. Then the statistically significant features were fed to four classifiers namely Decision Tree (DT), K-Nearest Neighbor (KNN), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) to select the best classifier. We observed that the SVM classifier with Radial Basis Function (RBF) kernel function yielded the best results with an average accuracy of 96%, average sensitivity of 96.9% and average specificity of 97% for 23.6 s duration of EEG data. Our proposed technique can be used as an automatic seizure monitoring software. It can also assist the doctors to cross check the efficacy of their prescribed drugs.

Intrinsic time-scale decomposition (ITD) is a new nonlinear method of time-frequency representation which can decipher the minute changes in the nonlinear EEG signals. In this work, we have automatically classified normal, interictal and ictal EEG signals using the features derived from the ITD representation. The energy, fractal dimension and sample entropy features computed on ITD representation coupled with decision tree classifier has yielded an average classification accuracy of 95.67%, sensitivity and specificity of 99% and 99.5%, respectively using 10-fold cross validation scheme. With application of the nonlinear ITD representation, along with conceptual advancement and improvement of the accuracy, the developed system is clinically ready for mass screening in resource constrained and emerging economy scenarios.

Electroencephalogram (EEG) is a record of ongoing electrical signal to represent the human brain activity. It has great potential for the diagnosis to treatment of mental disorder and brain diseases such as epileptic seizure. Features extraction and classification is a crucial task to detect the stage of ictal (i.e. seizure period) and interictal (i.e. period between seizures) EEG signals for the treatment and precaution of the patient. However, existing seizure and non-seizure feature extraction techniques are not good enough for the classification of ictal and interictal EEG signals considering their non-abrupt phenomena and inconsistency in different brain locations. In this paper, we present new approaches for feature extraction using high-frequency components from discrete cosine transformation (DCT) and intrinsic mode function (IMF) extracted from empirical mode decomposition (EMD). These features are then used as an input to least square-support vector machine (LV-SVM) to classify ictal and interictal EEG signals. Experimental results show that the proposed methods outperform the existing state-of-the-art method for better classification in terms of sensitivity, specificity, and accuracy with greater consistence of ictal and interictal period of epilepsy for benchmark dataset from different brain locations.

Epileptic seizure in the brain affects the day-to-day life of any individual due to its unexpected nature of occurrence. It has affected more than 50 million people worldwide. Drug resistance of patients is an important factor which leads to failure of epilepsy treatments using medications in 30% of patients. Surgery is also not a viable option in a substantial number of patients. In such cases, a new kind of seizure forecasting system is necessary to help those people. In our work, various sub-frequency bands of EEG signals are produced from the originally recorded Intracranial Electroencephalogram (IEEG) signals of five canines and two persons to identify possible low complex and less intense EEG features from each sub-band of the entire spectrum. Support Vector Machine (SVM) with different Kernel-based classifiers are used to categorize features into preictal and interictal data. Epileptic Seizures forecasting accuracy of 99% has been achieved for data from canine and human. Employed wavelet filter for noise removal and found that it improved the seizure prediction accuracy in some subjects and reduced the accuracy in some subjects. Similarly, the feature selection technique also improved the preictal detection accuracy in some patients/subjects and reduced the accuracy in some data. From this work, we identified that seizure prediction is possible in at least one sub-frequency band especially in high gamma sub-band generated from the originally recorded signal using a high-pass filter. This work demonstrates an algorithm for seizure forecasting or identifying the preictal region which identifies the suitable best sub-frequency band for predicting the seizure of the originally recorded EEG data by using the computationally less intense EEG features and employing the best classifying SVM kernel.