Detection of seizures in EEG using subband nonlinear parameters and genetic algorithm

Authors:
Kai-Cheng Hsu;Sung-Nien Yu
Affiliations:
Department of Electrical Engineering, National Chung Cheng University, 168 University Road, Ming-Hsiung Township, Chia-Yi County, Taiwan and Hsu Kai-Cheng Neurology Clinic, 26 Chung-Le Road, Ming- ...;Department of Electrical Engineering, National Chung Cheng University, 168 University Road, Ming-Hsiung Township, Chia-Yi County, Taiwan
Venue:
Computers in Biology and Medicine
Year:
2010

Citing 7
Cited 4

Practical method for determining the minimum embedding dimension of a scalar time series

Physica D
Support Vector Machines for 3D Object Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelet analysis of generalized tonic-clonic epileptic seizures

Signal Processing
Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction

Computers in Biology and Medicine
One-Class Novelty Detection for Seizure Analysis from Intracranial EEG

The Journal of Machine Learning Research
Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm

Signal Processing
Epileptic seizure detection using dynamic wavelet network

Expert Systems with Applications: An International Journal

Bispectral analysis and genetic algorithm for congestive heart failure recognition based on heart rate variability

Computers in Biology and Medicine
Seizure detection in clinical EEG based on entropies and EMD

ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Early detection of epileptic seizures based on parameter identification of neural mass model

Computers in Biology and Medicine
Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Detection of seizures in EEG can be challenging because of myogenic artifacts and might be time-consuming. In this study, we propose a method using subband nonlinear parameters and genetic algorithm for automatic seizure detection in EEG. In the experiment, the discrete wavelet transform was used to decompose EEG into five subband components. Nonlinear parameters were extracted and employed as the features to train the support vector machine with linear kernel function (SVML) and radial basis function kernel function (SVMRBF) classifiers. A genetic algorithm (GA) was used for selecting the effective feature subset. The seizure detection sensitivities of the SVML and the SVMRBF with GA are 90.8% and 94.0%, respectively. The sensitivity of SVMRBF increases to 95.8% by using GA for weight adjustment. Moreover, the proposed method is capable of discriminating the interictal EEG of epileptic subjects from the normal EEG, which is considered difficult, yet crucial, in clinical services.