Singular Lorenz Measures Method for seizure detection using KNN-Scatter Search optimization algorithm

Offline algorithm to detect the intractable epileptic seizure of children has vital role for surgical intervention. In this paper, after preprocessing and windowing procedure by Discrete Wavelet Transform (DWT), EEG signal is decomposed to five brain rhythms. These rhythms are formed to 2D pattern by upsampling idea. We have proposed a novel scenario for feature extraction that is called Singular Lorenz Measures Method (SLMM). In our method, by Chan's Singular Value Decomposition (Chan's SVD) in two phases including of QR factorization and Golub-Kahan-Reinsch algorithm, the singular values as energies of the signal on orthogonal space for pattern of rhythms in all windows are obtained. The Lorenz curve as a depiction of Cumulative Distribution Function (CDF) of singular values set is computed. With regard to the relative inequality measures, the Lorenz inconsistent and consistent features are extracted. Moreover, the hybrid approach of K-Nearest Neighbor (KNN) and Scatter Search (SS) is applied as optimization algorithm. The Multi-Layer Perceptron (MLP) neural network is also optimized on the hidden layer and learning algorithm. The optimal selected attributes using the optimized MLP classifier are employed to recognize the seizure attack. Ultimately, the seizure and non-seizure signals are classified in offline mode with accuracy rate of 90.0% and variance of MSE 1.47×10-4.