Optimized feature extraction process to identify the seizure using feature fusion technique

Epilepsy is a brain disorder that results in seizures; in general seizure is a suddenly occurring uncontrollable electrical disturbance in the brain. These disturbances in the brain can lead to changes in behaviour, feelings, movements, etc. It is highly essential for the patients suffering with epilepsy to be diagnosed and treated. The normal detection of epilepsy is done using EEG signals which are time consuming. This paper aims at proposing a methodology to diagnose epilepsy by the use of EEG signals by establishing a correlation between statistical calculations and EEG signals. A various set of features are applied to the epilepsy and non-epilepsy dataset. Features such as time domain frequency which include mean, skewness, variance, kurtosis, standard deviation, approximate entropy, zero crossings, power spectrum and frequency domain features that include signal energy and total signal area, average DWT coefficient, signal relation features and human brain graph features. Further, considering these features, feature fusion and optimiziation aka FFO is carried out which helps in analysing the features in optimal way for further classification. Moreover, feature fusion and its optimization helps in exploring the new features that helps in enhancing the distinguish between classes.These features help diagnosis of the brain disorder in a very time efficient manner with higher accuracy. In this paper, we propose a feature fusion methodology for the most efficient working of the system. Genetic Algorithm (GA). Machine Learning classifiers including ANN, KNN, NB and SVM are used for classification of the EEG signal to non-epileptic or epileptic. After the evaluation of these metrics, high accuracy has been obtained by ANN classifier along with the other combinations of datasets and this outperforms other classifiers. [21] introduces a technique of seizure recognition that is effective for epilepsy on the basis of Stockwell Transform (S-transform) and bidirectional long short-term memory (Bi-LSTM) neural networks for EEG recordings that are intracranial. Evaluation based on event and segment to evaluate signals has been done. Resulting in which the authors have obtained 98.09% sensitivity, 98.69% specificity, 96.3% sensitivity and 0.24/h false detection rate respectively. In [22] includes a framework which has two parts. The signal intensity is calculated in the first part for every data point, that enables the autoregressive moving average (ARMA) model used in characterizing dynamic behaviour for the time series of EEG. Secondly, pattern recognition technique has been used as a classification method and has an accuracy of 93% and 94% respectively. During this work, 0.317s of execution time was attained for the SVM model. A method that is based on extraction of training the EEG signals from epileptic characteristic waves in [23]. A set of the wavelets have been constructed for performing CWT on recorded signal of EEG. CWT coefficient matrices have data fusion applied to it that correspond to the wavelets that are multiple constructed which help determine the boundaries of the seizures. A system that is based on DWT analysis has been developed in [24] having EEG signals that use non-linear and linear classifiers for the detection of epileptic seizures. Statistical features are classified using Naïve Bayes and KNN classifiers that are obtained through DWT having accuracy of 100%. A system has been developed by the authors in [25] for the detection of epilepsy by the use of TF entropy features where S-Transform is used having 86%