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Dr Mashael Suliaman Maashi (BSc, MSc, PhD) دكتورة مشاعل بنت سليمان معشي

Associate Professor

Faculty, Director of the Research Center

علوم الحاسب والمعلومات
Building# 6, floor# 3, Office No#69
publication
Journal Article
2020

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

J., Prasanna . 2020

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 FastWalsh–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. Amaximumsensitivity 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 usingUniversity
of Bonn dataset. Compared to the existing technique, the proposed approach attained a maximum
classification performance in both the dataset.

Volume Number
20
Issue Number
17
Magazine \ Newspaper
Sensors
Pages
4952
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