Neural Networks with Periodogram and Autoregressive Spectral Analysis Methods in Detection of Epileptic Seizure

Authors:
M. Kemal Kiymik;Abdulhamit Subasi;H. Rı/za Ozcalı/k
Affiliations:
Department of Electrical and Electronics Engineering, Kahramanmaraş/ Sü/tç/ü/ İ/mam University, 46100 Kahramanmaras, Turkey/ mkemal@ksu.edu.tr;Department of Electrical and Electronics Engineering, Kahramanmaraş/ Sü/tç/ü/ İ/mam University, 46100 Kahramanmaras, Turkey;Department of Electrical and Electronics Engineering, Kahramanmaraş/ Sü/tç/ü/ İ/mam University, 46100 Kahramanmaras, Turkey
Venue:
Journal of Medical Systems
Year:
2004

Citing 8
Cited 10

Digital signal processing (3rd ed.): principles, algorithms, and applications

Digital signal processing (3rd ed.): principles, algorithms, and applications
Detection of seizure activity in EEG by an artificial neural network: a preliminary study

Computers and Biomedical Research
An adaptive structure neural networks with application to EEG automatic seizure detection

Neural Networks
Classification of EEG signals using the wavelet transform

Signal Processing
Mining multi-channel EEG for its information content: an ANN-based method for a brain-computer interface

Neural Networks - Special issue on neural control and robotics: biology and technology
Application of Periodogram and AR Spectral Analysis to EEG Signals

Journal of Medical Systems
An Artificial Neural Network Approach to Diagnosing Epilepsy Using Lateralized Bursts of Theta EEGs

Journal of Medical Systems
On-line spectral estimation of nonstationary time series based onAR model parameter estimation and order selection with a forgettingfactor

IEEE Transactions on Signal Processing

Application of Classical and Model-Based Spectral Methods to Describe the State of Alertness in EEG

Journal of Medical Systems
Artificial Neural Network Based Epileptic Detection Using Time-Domain and Frequency-Domain Features

Journal of Medical Systems
Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction

Computers in Biology and Medicine
Selection of optimal AR spectral estimation method for EEG signals using Cramer-Rao bound

Computers in Biology and Medicine
Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm

Signal Processing
Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy

Expert Systems with Applications: An International Journal
Expert model for detection of epileptic activity in EEG signature

Expert Systems with Applications: An International Journal
Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines

Expert Systems with Applications: An International Journal
Quality-on-demand compression of EEG signals for telemedicine applications using neural network predictors

International Journal of Telemedicine and Applications
Discrete harmony search based expert model for epileptic seizure detection in electroencephalography

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.01

Visualization

Abstract

Approximately 1% of the people in the world suffer from epilepsy. Careful analyses of the electroencephalograph (EEG) records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders. Predicting the onset of epileptic seizure is an important and difficult biomedical problem, which has attracted substantial attention of the intelligent computing community over the past two decades. The purpose of this work was to investigate the performance of the periodogram and autoregressive (AR) power spectrum methods to extract classifiable features from human electroencephalogram (EEG) by using artificial neural networks (ANN). The feedforward ANN system was trained and tested with the backpropagation algorithm using a large data set of exemplars. We present a method for the automatic comparison of epileptic seizures in EEG, allowing the grouping of seizures having similar overall patterns. Each channel of the EEG is first broken down into segments having relatively stationary characteristics. Features are then calculated for each segment, and all segments of all channels of the seizures of a patient are grouped into clusters of similar morphology. This clustering allows labeling of every EEG segment. Examples from 5 patients with scalp electrodes illustrate the ability of the method to group seizures of similar morphology. It was observed that ANN classification of EEG signals with AR preprocessing gives better results, and these results can also be used for the deduction of epileptic seizure.