Detection of epileptic seizure in EEG signals using linear least squares preprocessing

Detection of epileptic seizure in EEG signals using linear least squares preprocessing

Highlights • Linear least squares preprocessing models are developed for automatic detection of seizures. • They extract key features of an epileptic EEG signal. • They significantly reduce the dimension of the classification problem and the computational time. • They enhance the classification accu...

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Veröffentlicht in:Computer methods and programs in biomedicine 2016-09, Vol.133, p.95-109
1. Verfasser: Zamir, Z. Roshan
Format: Artikel
Sprache:eng
Schlagworte:
Accuracy
Biological signal classification
Classification
Classifiers
Data analysis
EEG seizure detection
Electroencephalography
Electroencephalography - methods
Epilepsy - physiopathology
Feature extraction
Humans
Internal Medicine
Least-Squares Analysis
Linear least squares problems
Mathematical models
Models, Theoretical
Other
Preprocessing
Seizing
Signal approximation
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Beschreibung
Zusammenfassung:Highlights • Linear least squares preprocessing models are developed for automatic detection of seizures. • They extract key features of an epileptic EEG signal. • They significantly reduce the dimension of the classification problem and the computational time. • They enhance the classification accuracy of an EEG signal in presence of seizures. • They are robust and efficient for detecting epileptic seizures.
ISSN:0169-2607
1872-7565
DOI:10.1016/j.cmpb.2016.05.002