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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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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