Abstract
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.
Highlights
Epilepsy is a nervous system syndrome originated by the frequent occurrence of the seizures [1]owing to the sudden abnormal flow of electrical activity in the central nervous system which resultsSensors 2020, 20, 4952; doi:10.3390/s20174952 www.mdpi.com/journal/sensorsSensors 2020, 20, 4952 in unconsciousness, disability in body movement [2], and sudden unexpected death in epilepsy (SUDEP) that occurs one in 1000 adults and one in 4500 children each year
The non-focal class (NFC) and focal class (FC) EEG signals acquired from BB and University of Bonn dataset datasets were employed to investigate the proposed methodology
The Hadamard coefficients in the individual pairs of the NFC and FC EEG signals were calculated by the effective decomposition method
Summary
Epilepsy is a nervous system syndrome originated by the frequent occurrence of the seizures [1]owing to the sudden abnormal flow of electrical activity in the central nervous system which resultsSensors 2020, 20, 4952; doi:10.3390/s20174952 www.mdpi.com/journal/sensorsSensors 2020, 20, 4952 in unconsciousness, disability in body movement [2], and sudden unexpected death in epilepsy (SUDEP) that occurs one in 1000 adults and one in 4500 children each year. Epilepsy is a nervous system syndrome originated by the frequent occurrence of the seizures [1]. Owing to the sudden abnormal flow of electrical activity in the central nervous system which results. League Against Epilepsy (ILAE), drug-resistant epilepsy is referred to as the epileptic seizures which are unresponsive to the antiepileptic drugs and which results in the treatment through surgery. The conventional EEG-based approach for epileptic diagnosis and estimation of EZ needs tiresome manual inspection of EEG signals done by eminently qualified neurologists [5], an increasing demand to automate it is on the rise. To assist the neurologists in identifying the EZ, many computer-aided automated diagnosis tools have been developed using signal processing techniques and machine learning algorithms
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