ArticleHealth information science and systems2017
An optimum allocation sampling based feature extraction scheme for distinguishing seizure and seizure-free EEG signals.
Article in Health information science and systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- GEM-CRAP: a fusion architecture for focal seizure detection.Journal of translational medicine · 2025Article
- SchizoGoogLeNet: The GoogLeNet-Based Deep Feature Extraction Design for Automatic Detection of Schizophrenia.Computational intelligence and neuroscience · 2022Article
- Classification of normal and depressed EEG signals based on centered correntropy of rhythms in empirical wavelet transform domain.Health information science and systems · 2021Article
- An efficient approach for physical actions classification using surface EMG signals.Health information science and systems · 2020Article
- Wavelet based deep learning approach for epilepsy detection.Health information science and systems · 2019Article
- A performance based feature selection technique for subject independent MI based BCI.Health information science and systems · 2019Article
- Real-time epileptic seizure prediction based on online monitoring of pre-ictal features.Medical & biological engineering & computing · 2019Article
- Guest editorial: special issue on "Artificial Intelligence in Health and Medicine".Health information science and systems · 2018Article
- Emotion classification using flexible analytic wavelet transform for electroencephalogram signals.Health information science and systems · 2018Article
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3 authors.
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Abstract
Epileptic seizure is the common neurological disorder, which is generally identified by electroencephalogram (EEG) signals. In this paper, a new feature extraction methodology based on optimum allocation sampling (OAS) and Teager energy operator (TEO) is proposed for detection of seizure EEG signals. The OAS scheme selects the finite length homogeneous sequence from non-homogeneous recorded EEG signal. The trend of selected sequence by OAS is still non-linear, which is analyzed by non-linear operator TEO. The TEO convert non-linear but homogenous EEG sequence into amplitude-frequency modulated (AM-FM) components. The statistical measures of AM-FM components used as input features to least squares support vector machine classifier for classification of seizure and seizure-free EEG signals. The proposed methodology is evaluated on a benchmark epileptic seizure EEG database. The experimental results demonstrate that the proposed scheme has capability to effectively distinguish seizure and seizure-free EEG signals.
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