RETRACTED ARTICLE: Noninvasive method of epileptic detection using DWT and generalized regression neural network

Epilepsy is a continual disorder, the characteristic of which is recurrent, motiveless seizures. Many people with epilepsy have more than one type of seizure and may have other symptoms of neurological problems as well. In this paper, a noninvasive method using discrete wavelet transform (DWT) and n...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2019-04, Vol.23 (8), p.2645-2653
Main Authors: Vijay Anand, S., Shantha Selvakumari, R.
Format: Article
Language:English
Subjects:
Accuracy
Anomalies
Approximation
Artificial Intelligence
Artificial neural networks
Classification
Computational Intelligence
Control
Convulsions & seizures
Datasets
Decomposition
Discrete Wavelet Transform
Discriminant analysis
Electroencephalography
Engineering
Entropy
Epilepsy
Error analysis
Focus
Fourier transforms
Mathematical Logic and Foundations
Mechatronics
Neural networks
Neurological disorders
Parameter sensitivity
Robotics
Seizures
Signal classification
Signal processing
Statistical analysis
Support vector machines
Wavelet transforms
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Summary:Epilepsy is a continual disorder, the characteristic of which is recurrent, motiveless seizures. Many people with epilepsy have more than one type of seizure and may have other symptoms of neurological problems as well. In this paper, a noninvasive method using discrete wavelet transform (DWT) and neural network is projected for automatic detection of epilepsy from EEG signals. DWT of the EEG signals is carried out using Haar Wavelets. Statistical features of approximate and detailed coefficients are extracted from the transformed signal. The entropy, as well as approximate entropy of the transformed signals, is determined. The features extracted from the transformed signal are used as the training set for the artificial neural network (ANN). Two types of ANNs viz. feedforward neural network (FFNN) and generalized regression neural network (GRNN) are trained. Three types of subjects viz. healthy, seizure-free period of an epileptic patient and epileptic patients are considered. The signals are classified accordingly as normal, seizure-free epileptic and abnormal. The results are compared on the basis of the confusion matrix, error histogram, and error plot. The quality measures used for comparison are sensitivity, specificity, precision, and accuracy. On all the evaluation parameters, GRNN is found to be best suited for anomaly detection in EEG signals.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-018-3630-y