NDLI: Multi-kernel-based random vector functional link network with decomposed features for epileptic EEG signal classification

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Multi-kernel-based random vector functional link network with decomposed features for epileptic EEG signal classification

Content Provider	IET Digital Library
Author	Parija, Sebamai Dash, Pradipta Kishore Bisoi, Ranjeeta
Abstract	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.
Starting Page	162
Ending Page	174
Page Count	13
ISSN	17519675
Volume Number	14
e-ISSN	17519683
Issue Number	Issue 3, May (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/14/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2019.0277
Journal	IET Signal Processing
Publisher Date	2020-02-03
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bioelectric Signal Biology And Medical Computing Computer Vision And Image Processing Technique Decomposed Features Digital Signal Processing Efficient Optimisation Algorithm Electrical Activity in Neurophysiological Processes Electrodiagnostics And Other Electrical Measurement Technique Electroencephalography Empirical Mode Decomposition Entropy Entropy Based Feature Epileptic EEG Signal Epileptic EEG Signal Classification Epileptic Electroencephalogram Signal Feature Extraction Image Classification Important Statistical Based Feature Improved Hybrid Model Intrinsic Mode Function Kernel Function Kernel Parameter Knowledge Engineering Technique Medical Signal Processing Multikernel Random Vector Functional Link Network Multikernel-based Random Vector Functional Link Network Optimisation Sensitivity Analysis Signal Classification Signal Processing And Detection Statistics Support Vector Machine Water Cycle Algorithm Wavelet Transform WCA–EMD–WMKRVFLN
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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