Abstract
BackgroundEpilepsy is a neurological disorder from which almost 50 million people have been suffering. These statistics indicate the importance of epilepsy diagnosis. Electroencephalogram (EEG) signals analysis is one of the most common methods for epilepsy characterization; hence, various strategies were applied to classify epileptic EEGs.MethodsIn this paper, four different nonlinear features such as Fractal dimensions including Higuchi method (HFD) and Katz method (KFD), Hurst exponent, and L-Z complexity measure were extracted from EEGs and their frequency sub-bands. The features were ranked later by implementing Relieff algorithm. The ranked features were applied sequentially to three different classifiers (MLPNN, Linear SVM, and RBF SVM).ResultsAccording to the dataset used for this study, there are five classification problems named ABCD/E, AB/CD/E, A/D/E, A/E, and D/E. In all cases, MLPNN was the most accurate classifier. Its performances for mentioned classification problems were 99.91%, 98.19%, 98.5%, 100% and 99.84%, respectively.ConclusionThe results demonstrate that KFD is the highest-ranking feature; In addition, beta and theta sub-bands are the most important frequency bands because, for all cases, the top features were KFDs extracted from beta and theta sub-bands. Moreover, high levels of accuracy have been obtained just by using these two features which reduce the complexity of the classification.
Highlights
The human brain is a complex system and displays temporally intricate dynamics
Multi‐layer perceptron neural networks (MLPNN) had the best performance, when just the first selected feature was used, the accuracy of this classifier was significantly lower than other classifiers
Our results indicate that nonlinear features can categorize different classification problems with high accuracy and Katz fractal dimension (KFD) is a paramount measure to classify EEGs
Summary
The human brain is a complex system and displays temporally intricate dynamics. One way to observe the brain’s activity is Electroencephalography. EEG signals are the recording of the brain’s electrical activity and are used by clinicians in the diagnosis of neurological disorders [1]. EEG signals have valuable information about this disorder. Detecting abnormality in EEGs is a critical issue in the diagnosis process. Since visual inspection is not a proper and reliable method to detect abnormality in EEGs, various methods are presented to extract important features. Epilepsy is a neurological disorder from which almost 50 million people have been suffering. These statistics indicate the importance of epilepsy diagnosis. Electroencephalogram (EEG) signals analysis is one of the most common methods for epilepsy characterization; various strategies were applied to classify epileptic EEGs
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