Noninvasive method of epileptic detection using DWT and generalized regression neural network
| Autor: | S. Vijay Anand, R. Shantha Selvakumari |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Discrete wavelet transform
0209 industrial biotechnology Computer science Physics::Medical Physics 02 engineering and technology Electroencephalography Approximate entropy Theoretical Computer Science Epilepsy 020901 industrial engineering & automation Wavelet 0202 electrical engineering electronic engineering information engineering medicine Entropy (information theory) Entropy (energy dispersal) Training set Quantitative Biology::Neurons and Cognition Artificial neural network medicine.diagnostic_test business.industry Confusion matrix Pattern recognition medicine.disease Feedforward neural network 020201 artificial intelligence & image processing Geometry and Topology Artificial intelligence business Software |
| Zdroj: | Soft Computing. 23:2645-2653 |
| ISSN: | 1433-7479 1432-7643 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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