Open AccessAutomated Epileptic Seizure Detection Model using WPT, CFS and KNN based Multiclass TSVM
Author(s) -
Sumant Kumar Mohapatra,
Madhusmita Mohanty,
Biswa Ranjan Swain,
electronics B.E
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.b4571.029320
Subject(s) - electroencephalography , ictal , epileptic seizure , pattern recognition (psychology) , support vector machine , artificial intelligence , computer science , sensitivity (control systems) , epilepsy , psychology , engineering , neuroscience , electronic engineering
The main aim of the proposed work is to generate an accurate automated seizure detection model for the performance evaluation of the improvement on epileptic patients in an improved manner. Long data sets of EEG signals are recorded for a long duration of time which has taken from PhysioNet CHB-MIT EEG datset for this experimental work. Six types of elements are excerpted from EEG signals by using WPT method and which is then classified by using CFS method. Then, all the features are combinely inputted to the rule based twin- support vector machines (TSVMs ) to detect normal, ictal and pre-ictal EEG segments. The developed seizure detection WPT-KWMTSVM method achieved excellent performance with the average Accuracy, specificity, sensitivity, G-mean, positive predictive value, and Mathews correlation coefficients are 97.14%, 97.33%, 97.00%, 97.31%, 96.85%, 95.96% respectively The average area under curve (AUC) is approximately 1. The proposed method is able to enhance the seizure detection outcomes for proper clinical diagnosis in medical applications.