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An epileptic seizure is a transient event of abnormal excessive neuronaldischarge in the brain. This unwanted event can be obstructed by detection ofelectrical changes in the brain that happen before the seizure takes place. Theautomatic detection of seizures is necessary since the visual screening of EEGrecordings is a time consuming task and requires experts to improve thediagnosis. Four linear least squares-based preprocessing models are proposed toextract key features of an EEG signal in order to detect seizures. The firsttwo models are newly developed. The original signal (EEG) is approximated by asinusoidal curve. Its amplitude is formed by a polynomial function and comparedwith the pre developed spline function.Different statistical measures namelyclassification accuracy, true positive and negative rates, false positive andnegative rates and precision are utilized to assess the performance of theproposed models. These metrics are derived from confusion matrices obtainedfrom classifiers. Different classifiers are used over the original dataset andthe set of extracted features. The proposed models significantly reduce thedimension of the classification problem and the computational time while theclassification accuracy is improved in most cases. The first and third modelsare promising feature extraction methods. Logistic, LazyIB1, LazyIB5 and J48are the best classifiers. Their true positive and negative rates are $1$ whilefalse positive and negative rates are zero and the corresponding precisionvalues are $1$. Numerical results suggest that these models are robust andefficient for detecting epileptic seizure.

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