Complexity analysis in focal epilepsy using entropy methods

Epilepsy is a neurological disorder characterized by abnormal electrical activity of neurons affecting a small or large area of the brain. Epilepsy is categorized into focal epilepsy and generalized epilepsy. Focal epilepsy is due to abnormal electrical activity that effects the smaller brain sections, whereas in generalized epilepsy the larger section of the brain is affected. Epilepsy affects the patient's personality, behavior, and emotions, making daily routines difficult. Early detection is therefore essential. In this study, we use electroencephalogram (EEG) signals to detect the focal epilepsy. However, due to the random and non-stationary characteristics it is difficult to analyze the subtle changes in the EEG by visual inspection. Hence, in this study we proposed a system that classifies focal epileptic and normal signals accurately. In order to analyze the difference in the randomness of the focal epileptic and the normal signals we have used fuzzy approximation entropy and spectral entropy. The system first decomposes the signal, and then features are extracted using fuzzy approximation entropy and spectral entropy. We obtain classification accuracies of 95% and 85% using 4-nearest neighbor and support vector machine classifiers, respectively.