Open AccessEpileptic EEG signal classifications based on DT-CWT and SVM classifier
Author(s) -
Senthilkumar Deivasigamani,
C. Senthilpari,
Wong Hin Yong,
Perumbilavil Kaithamanakallam Rajesh
Publication year - 2021
Publication title -
maǧallaẗ al-abḥāṯ al-handasiyyaẗ
Language(s) - English
Resource type - Journals
eISSN - 2307-1885
pISSN - 2307-1877
DOI - 10.36909/jer.10523
Subject(s) - electroencephalography , support vector machine , computer science , artificial intelligence , epilepsy , pattern recognition (psychology) , classifier (uml) , cerebrum , speech recognition , neuroscience , psychology , central nervous system
Contamination in human cerebrum causes the mind issue which is as Epilepsy. The contaminated territory in the cerebrum area creates the unpredictable example signals as focal signs and the other sound locales in the mind produce the standard example signals as non-focal sign. Henceforth, the discovery of focal signs from the non-focal signs is a significant for epileptic medical procedure in epilepsy patients. This paper proposes a straightforward and proficient technique for EEG (Electroencephalogram) signals orders utilizing SVM (Support Vector Machine) classifier. The exhibition of the proposed EEG signals characterization framework is assessed as far as Sensitivity, Specificity, and Accuracy.