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Literature DB >> 17959496

Seizure detection in EEG signals: a comparison of different approaches.

Hamid R Mohseni¹, A Maghsoudi, Mohammad B Shamsollahi.

Abstract

In this paper, the performance of traditional variance-based method for detection of epileptic seizures in EEG signals are compared with various methods based on nonlinear time series analysis, entropies, logistic regression,discrete wavelet transform and time frequency distributions.We noted that variance-based method in compare to the mentioned methods had the best result (100%) applied on the same database.

Entities: Disease

Mesh：

Year: 2006 PMID： 17959496 DOI： 10.1109/IEMBS.2006.260931

Source DB: PubMed Journal: Conf Proc IEEE Eng Med Biol Soc ISSN： 1557-170X

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Cited

9 in total

1. A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms.

Authors: Baha Şen; Musa Peker; Abdullah Çavuşoğlu; Fatih V Çelebi
Journal: J Med Syst Date: 2014-03-09 Impact factor: 4.460

Seizure detection in EEG signals: a comparison of different approaches.

1. A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms.

2. New feature extraction approach for epileptic EEG signal detection using time-frequency distributions.

3. Emotion recognition using effective connectivity and pre-trained convolutional neural networks in EEG signals.

4. Mental Arithmetic Task Recognition Using Effective Connectivity and Hierarchical Feature Selection From EEG Signals.

5. Topological Data Analysis of Single-Trial Electroencephalographic Signals.

6. Efficient FIR Filter Implementations for Multichannel BCIs Using Xilinx System Generator.

7. Dual deep neural network-based classifiers to detect experimental seizures.

Review 8. Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches.

9. Enhanced Feature Extraction-based CNN Approach for Epileptic Seizure Detection from EEG Signals.