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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| Published in | Computer methods and programs in biomedicine Vol. 133; pp. 95 - 109 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
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Elsevier B.V
01.09.2016
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| Abstract | 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. |
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| AbstractList | 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. •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 the presence of seizures.•They are robust and efficient for detecting epileptic seizures. An epileptic seizure is a transient event of abnormal excessive neuronal discharge in the brain. This unwanted event can be obstructed by detection of electrical changes in the brain that happen before the seizure takes place. The automatic detection of seizures is necessary since the visual screening of EEG recordings is a time consuming task and requires experts to improve the diagnosis. Much of the prior research in detection of seizures has been developed based on artificial neural network, genetic programming, and wavelet transforms. Although the highest achieved accuracy for classification is 100%, there are drawbacks, such as the existence of unbalanced datasets and the lack of investigations in performances consistency. To address these, four linear least squares-based preprocessing models are proposed to extract key features of an EEG signal in order to detect seizures. The first two models are newly developed. The original signal (EEG) is approximated by a sinusoidal curve. Its amplitude is formed by a polynomial function and compared with the predeveloped spline function. Different statistical measures, namely classification accuracy, true positive and negative rates, false positive and negative rates and precision, are utilised to assess the performance of the proposed models. These metrics are derived from confusion matrices obtained from classifiers. Different classifiers are used over the original dataset and the set of extracted features. The proposed models significantly reduce the dimension of the classification problem and the computational time while the classification accuracy is improved in most cases. The first and third models are promising feature extraction methods with the classification accuracy of 100%. Logistic, LazyIB1, LazyIB5, and J48 are the best classifiers. Their true positive and negative rates are 1 while false positive and negative rates are 0 and the corresponding precision values are 1. Numerical results suggest that these models are robust and efficient for detecting epileptic seizure. An epileptic seizure is a transient event of abnormal excessive neuronal discharge in the brain. This unwanted event can be obstructed by detection of electrical changes in the brain that happen before the seizure takes place. The automatic detection of seizures is necessary since the visual screening of EEG recordings is a time consuming task and requires experts to improve the diagnosis. Much of the prior research in detection of seizures has been developed based on artificial neural network, genetic programming, and wavelet transforms. Although the highest achieved accuracy for classification is 100%, there are drawbacks, such as the existence of unbalanced datasets and the lack of investigations in performances consistency. To address these, four linear least squares-based preprocessing models are proposed to extract key features of an EEG signal in order to detect seizures. The first two models are newly developed. The original signal (EEG) is approximated by a sinusoidal curve. Its amplitude is formed by a polynomial function and compared with the predeveloped spline function. Different statistical measures, namely classification accuracy, true positive and negative rates, false positive and negative rates and precision, are utilised to assess the performance of the proposed models. These metrics are derived from confusion matrices obtained from classifiers. Different classifiers are used over the original dataset and the set of extracted features. The proposed models significantly reduce the dimension of the classification problem and the computational time while the classification accuracy is improved in most cases. The first and third models are promising feature extraction methods with the classification accuracy of 100%. Logistic, LazyIB1, LazyIB5, and J48 are the best classifiers. Their true positive and negative rates are 1 while false positive and negative rates are 0 and the corresponding precision values are 1. Numerical results suggest that these models are robust and efficient for detecting epileptic seizure. |
| Author | Zamir, Z. Roshan |
| Author_xml | – sequence: 1 fullname: Zamir, Z. Roshan |
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| Keywords | 92C55 Signal approximation Data analysis 65K05 Biological signal classification EEG Seizure detection 90C25 Feature extraction 65D15 65D07 Linear least squares problems EEG seizure detection |
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| Snippet | Highlights • Linear least squares preprocessing models are developed for automatic detection of seizures. • They extract key features of an epileptic EEG... •Linear least squares preprocessing models are developed for automatic detection of seizures.•They extract key features of an epileptic EEG signal.•They... An epileptic seizure is a transient event of abnormal excessive neuronal discharge in the brain. This unwanted event can be obstructed by detection of... |
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| SubjectTerms | 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 |
| Title | Detection of epileptic seizure in EEG signals using linear least squares preprocessing |
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