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.
Highlights
Despite the availability of drug and surgical treatment options, epilepsy manifests 1% of the world population [1] as mental and neurological disorder
All the classifiers were tuned with different parameters to view their performance using various metrics like weight, distance, learning rate, etc. that will help to investigation their performance in research
We have further evaluated the performance of support vector machine (SVM) Quadratic kernel in more depth by varying the kernel scales (KS) and fixing box constraint level (BCL) at 1
Summary
Despite the availability of drug and surgical treatment options, epilepsy manifests 1% of the world population [1] as mental and neurological disorder. Epilepsy stood fourth most common neurological syndrome after migraine, spike, and. The associate editor coordinating the review of this manuscript and approving it for publication was Quan Zou. Alzheimer’s disease with approximately 2.4 Million people newly diagnosed annually in the world. Chronic, and recurring neurological disorder hallmarked by frequent unpredictable seizures. Epileptic seizure transpires owing to the abrupt malfunctioning and synchronization of neurons, thereby imitating the excessive and hypersynchronous neuronal activity in the brain [2]. Epileptic seizures do not strike randomly, instead, they emerge from slow
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
