Epileptic Seizure Detection With Permutation Fuzzy Entropy Using Robust Machine Learning Techniques

Abstract

The automatic and accurate determination of the epileptogenic area can assist doctors in presurgical evaluation by providing higher security and quality of life. Visual inspection of electroencephalogram (EEG) signals is expensive, time-consuming and prone to errors. Several numbers of automated seizure detection frameworks were proposed to replace the traditional methods and to assist neurophysiologists in identifying epileptic seizures accurately. However, these systems lagged in achieving high performance due to the anti-noise ability of feature extraction techniques, while EEG signals are highly susceptible to noise during acquisition. The present study put forwards a new entropy index Permutation Fuzzy Entropy (PFEN), which may delineate between ictal and interictal state of epileptic seizure using different machine learning classifiers. 10-fold cross-validation has been used to avoid the over-fitting of the classification model to achieve unbiased, stable, and reliable performance. The proposed index correctly distinguishes ictal and interictal states with an average accuracy of 98.72%, sensitivity of 98.82% and a specificity of 98.63%, across 21 patients with six epileptic seizure origins. The proposed system manifests the fact that lower PFEN characterizes the EEG during seizure state than in the Interictal seizure state. The study also helps us to investigate the more profound enactment of different classifiers in term of their distance metrics, learning rate, distance, weights, multiple scales, etc. rather than the conventional methods in the literature. Compared to other state of art entropy-based feature extraction methods, PFEN showed its potential to be a promising non-linear feature for achieving high accuracy and efficiency in seizure detection. It also show’s its feasibility towards the development of a real-time EEG-based brain monitoring system for epileptic seizure detection.