Comprehensive Performance Analysis of Classifiers in Diagnosis of Epilepsy

Abstract

Epilepsy becomes one of the most frequently arising brain disorder, and it is marked by the unexpected occurrence of frequent seizures. In this study, the University of the Boon Database with ictal seizure disorder diagnosis of the epilepsy is classified by making use of the expectation maximization features as dimensionality reduction technique followed by the nonlinear model, namely, Gaussian mixture model, logistic regression, firefly algorithm, and hybrid model such as cuckoo search with Gaussian mixture model and firefly algorithm with the Gaussian mixture model which are the classifiers used for the diagnosis of epilepsy from the electroencephalogram signals. The performance of the classifiers is analyzed based on performance index, sensitivity, specificity, accuracy, mean square error, good detection rate, and error rate. The most promising outcome in this work indicates expectation maximization features are applied as the dimensionality reduction technique and the hybrid model Cuckoo search with the Gaussian mixture model outperforms with classification accuracy of 92.19%, performance index of 81.43%, good detection rate of 83.48%, and with low error rate of 15.62%, among other classifiers.

Epileptic Seizures Detection from EEG Recordings Based on a Hybrid system of Gaussian Mixture Model and Random Forest Classifier

“…In [13], the EEG data were entered directly into the classifiers, while in our system, dimension reduction and feature extraction were used before the classification phase. Overall, our proposed hybrid system achieved the highest accuracy of 93.62% compared with [11], [12], and [13]. In addition, the two studies in [14] and [15] used a different EEG dataset.…”

“…And As for the comparison with the related works [11], [12], [13], [14], and [15] that are illustrated in Table 1, it is important to say that our proposed hybrid system work is different from the previous studies presented in the field of detecting epileptic seizures (mentioned previously). In [11], the GMM was implemented as a classifier, in contrast to our proposed method, where it was used as a clustering algorithm. In [13], the EEG data were entered directly into the classifiers, while in our system, dimension reduction and feature extraction were used before the classification phase.…”

“…The authors in [11] utilized the Boon university EEG dataset to propose a technique for diagnosing epileptic patients' from EEG signals. First, they used expectationmaximization features to reduce the dimensions of the EEG dataset.…”

Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection

“…FA is a nature-inspired heuristic methodology developed by Yang in 2008 [37]. Due to its merit and optimization accuracy, it is widely adopted by researchers to Computational Intelligence and Neuroscience fnd solutions for a number of optimization problems [38][39][40][41][42][43]. Te FA is developed by mimicking the social behaviors found in frefies.…”