Computational model for detection of abnormal brain connections in children with autism

Abstract

In neuropsychological disorders significant abnormalities in brain connectivity are observed in some regions. A novel model demonstrates connectivity between different brain regions in children with autism. Wavelet decomposition is used to extract features such as relative energy and entropy from electroencephalograph signals. These features are used as input to a 3Dcellular neural network model that indicates brain connectivity. Results show significant differences and abnormalities in the left hemisphere, (p < 0.05) at electrodes AF3, F3, P7, T7, and O1 in the alpha band, AF3, F7, T7, and O1 in the beta band, and T7 and P7 in the gamma band for children with autism when compared with non-autistic controls. Abnormalities in the connectivity of frontal and parietal lobes and the relations of neighboring regions for all three bands (particularly the gamma band) were detected for autistic children. Evaluation demonstrated the alpha frequency band had the best level of distinction (96.6%) based on the values obtained from a cellular neural network that employed support vector machine methods.Many investigators have studied the EEGs and analyzed the signals of autistic subjects [14][15][16]. Sheikhani et al.[15] conducted a study of EEG signals based on Lempel-Ziv frequency methods (LZ) and the short-time Fourier transform (STFT). After evaluating results,