Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network

The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the neurologist has to visually examine the long hour electroencephalogram (EEG) signals, which consumes time and is prone t...

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Main Authors: Prasanna J., M. S. P. Subathra, Mazin Abed Mohammed, Mashael S. Maashi, Begonya Garcia-Zapirain, N. J. Sairamya, S. Thomas George
Format: Article
Language:English
Published: MDPI AG 2020-09-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/20/17/4952
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spelling doaj-a1e226719b8b430e84d6bcccad7913902020-11-25T03:39:59ZengMDPI AGSensors1424-82202020-09-01204952495210.3390/s20174952Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural NetworkPrasanna J.0M. S. P. Subathra1Mazin Abed Mohammed2Mashael S. Maashi3Begonya Garcia-Zapirain4N. J. Sairamya5S. Thomas George6Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Tamil Nadu 641114, IndiaDepartment of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Tamil Nadu 641114, IndiaCollege of Computer Science and Information Technology, University of Anbar, 11, Ramadi, Anbar, IraqSoftware Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaEvida Lab, University of Deusto, Avada/Univesidades 24, 48007 Bilbao, SpainDepartment of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Tamil Nadu 641114, IndiaDepartment of Biomedical Engineering, Karunya Institute of Technology and Sciences, Tamil Nadu 641114, IndiaThe discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the neurologist has to visually examine the long hour electroencephalogram (EEG) signals, which consumes time and is prone to error. Hence, in this present work, automated diagnosis of FC EEG signals from NFC EEG signals is developed using the Fast Walsh–Hadamard Transform (FWHT) method, entropies, and artificial neural network (ANN). The FWHT analyzes the EEG signals in the frequency domain and decomposes it into the Hadamard coefficients. Five different nonlinear features, namely approximate entropy (ApEn), log-energy entropy (LogEn), fuzzy entropy (FuzzyEn), sample entropy (SampEn), and permutation entropy (PermEn) are extracted from the decomposed Hadamard coefficients. The extracted features detail the nonlinearity in the NFC and the FC EEG signals. The judicious entropy features are supplied to the ANN classifier, with a 10-fold cross-validation method to classify the NFC and FC classes. Two publicly available datasets such as the University of Bonn and Bern-Barcelona dataset are used to evaluate the proposed approach. A maximum sensitivity of 99.70%, the accuracy of 99.50%, and specificity of 99.30% with the 3750 pairs of NFC and FC signal are achieved using the Bern-Barcelona dataset, while the accuracy of 92.80%, the sensitivity of 91%, and specificity of 94.60% is achieved using University of Bonn dataset. Compared to the existing technique, the proposed approach attained a maximum classification performance in both the dataset.https://www.mdpi.com/1424-8220/20/17/4952fast Walsh–Hadamard transformfeature extractionentropyclassificationartificial neural network
collection DOAJ
language English
format Article
sources DOAJ
author Prasanna J.
M. S. P. Subathra
Mazin Abed Mohammed
Mashael S. Maashi
Begonya Garcia-Zapirain
N. J. Sairamya
S. Thomas George
spellingShingle Prasanna J.
M. S. P. Subathra
Mazin Abed Mohammed
Mashael S. Maashi
Begonya Garcia-Zapirain
N. J. Sairamya
S. Thomas George
Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network
Sensors
fast Walsh–Hadamard transform
feature extraction
entropy
classification
artificial neural network
author_facet Prasanna J.
M. S. P. Subathra
Mazin Abed Mohammed
Mashael S. Maashi
Begonya Garcia-Zapirain
N. J. Sairamya
S. Thomas George
author_sort Prasanna J.
title Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network
title_short Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network
title_full Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network
title_fullStr Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network
title_full_unstemmed Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network
title_sort detection of focal and non-focal electroencephalogram signals using fast walsh-hadamard transform and artificial neural network
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2020-09-01
description The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the neurologist has to visually examine the long hour electroencephalogram (EEG) signals, which consumes time and is prone to error. Hence, in this present work, automated diagnosis of FC EEG signals from NFC EEG signals is developed using the Fast Walsh–Hadamard Transform (FWHT) method, entropies, and artificial neural network (ANN). The FWHT analyzes the EEG signals in the frequency domain and decomposes it into the Hadamard coefficients. Five different nonlinear features, namely approximate entropy (ApEn), log-energy entropy (LogEn), fuzzy entropy (FuzzyEn), sample entropy (SampEn), and permutation entropy (PermEn) are extracted from the decomposed Hadamard coefficients. The extracted features detail the nonlinearity in the NFC and the FC EEG signals. The judicious entropy features are supplied to the ANN classifier, with a 10-fold cross-validation method to classify the NFC and FC classes. Two publicly available datasets such as the University of Bonn and Bern-Barcelona dataset are used to evaluate the proposed approach. A maximum sensitivity of 99.70%, the accuracy of 99.50%, and specificity of 99.30% with the 3750 pairs of NFC and FC signal are achieved using the Bern-Barcelona dataset, while the accuracy of 92.80%, the sensitivity of 91%, and specificity of 94.60% is achieved using University of Bonn dataset. Compared to the existing technique, the proposed approach attained a maximum classification performance in both the dataset.
topic fast Walsh–Hadamard transform
feature extraction
entropy
classification
artificial neural network
url https://www.mdpi.com/1424-8220/20/17/4952
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