Open AccessMulti‐kernel‐based random vector functional link network with decomposed features for epileptic EEG signal classification
Author(s) -
Parija Sebamai,
Dash Pradipta Kishore,
Bisoi Ranjeeta
Publication year - 2020
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2019.0277
Subject(s) - pattern recognition (psychology) , artificial intelligence , support vector machine , hilbert–huang transform , electroencephalography , computer science , kernel (algebra) , entropy (arrow of time) , mathematics , psychology , physics , filter (signal processing) , combinatorics , quantum mechanics , psychiatry , computer vision
This study proposes an improved hybrid model built with empirical mode decomposition (EMD) features combined with weighted multi‐kernel random vector functional link network (WMKRVFLN) where the kernel parameters are optimised with an efficient optimisation algorithm known as water cycle algorithm (WCA) for diagnosis and classification of epileptic electroencephalogram (EEG) signals. The proposed model with optimisation is known as WCA–EMD–WMKRVFLN. The tanh and wavelet kernel functions are contributing together to the effectiveness of the proposed model. The features generated from EMD in terms of intrinsic mode functions (IMFs) are modulated to find important statistical and entropy based features and these features in a reduced form are employed as inputs to the model to classify epileptic EEG signals. The presented approach is evaluated in terms of percentage correct classification accuracy (ACC), specificity and sensitivity using two datasets and is compared with different classifiers and state‐of‐the‐art techniques. The highest accuracies of 99.69% (five‐class) and 100% (three‐class) achieved using the Bonn‐University dataset and 99.0% ACC (two‐class) achieved using the Bern‐Barcelona dataset. The achieved results report that the presented approach is a promising approach for EEG signal classification and is superior to several state‐of‐the‐art techniques and is highly comparable to many such techniques.