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Epileptic seizure detection using AR model on EEG signals

Mousavi, R ; Sharif University of Technology | 2008

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  1. Type of Document: Article
  2. DOI: 10.1109/CIBEC.2008.4786067
  3. Publisher: 2008
  4. Abstract:
  5. This study presents a new method for epilepsy detection based on autoregressive (AR) estimation of EEG signals. In this method, optimum order for AR model is determined by Bayesian Information Criterion (BIC) and then AR parameters of EEG signals (from EEG data set of epilepsy center of the University of Bonn, Germany) and their sub-bands (created with the help of wavelet decomposition) are extracted based on it. These parameters are used as a feature to classify the EEG signals into Healthy, Interictal (seizure free) and Ictal (during a seizure) groups using multilayer perceptron (MLP) classifier. Correct classification scores at the range of 91% to 96% reveals the potential of our approach for epilepsy detection. © 2008 IEEE
  6. Keywords:
  7. Argon ; Biomedical engineering ; Electroencephalography ; Neural networks ; AR model ; AR parameters ; Autoregressive estimations ; Bayesian information criterions ; BIC criterion ; Data sets ; EEG signals ; Epilepsy ; Epilepsy detections ; Epileptic seizure detections ; Germany ; Multi-Layer Perceptron ; Sub-bands ; Wavelet decomposition
  8. Source: 2008 Cairo International Biomedical Engineering Conference, CIBEC 2008, Cairo, 18 December 2008 through 20 December 2008 ; February , 2008 ; 9781424426959 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4786067?signout=success