Quantitative electroencephalography (qEEG) has been used as a tool for neurophysiologic diagnostic. We used spectrogram and coherence values for evaluating qEEG in 17 children (13 boys and 4 girls aged between 6 and 11) with autism disorders (ASD) and 11 control children (7 boys and 4 girls with the same age range). Evaluation of qEEG with statistical analysis demonstrated that alpha frequency band (8-13 Hz) had the best distinction level of 96.4% in relaxed eye-opened condition using spectrogram criteria. The ASD group had significant lower spectrogram criteria values in left brain hemisphere, (p < 0.01) at F3 and T3 electrodes and (p < 0.05) at FP1, F7, C3, Cz and T5 electrodes. Coherence values at 171 pairs of EEG electrodes indicated that there are more abnormalities with higher values in the connectivity of temporal lobes with other lobes in gamma frequency band (36-44 Hz).

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Bipolar disorder (BD) is a severe psychiatric disorder and has two common types: type I and type II. Early diagnosis of the subtypes is very challenging particularly in adolescence. In this study, 38 adolescents are participated including 18 patients with BD I and 20 patients with BD II. The electroencephalogram signal is recorded by 19 electrodes in open eyes at resting state. After preprocessing, the state of the art methods from various domains are implemented to provide a good feature set for classifying the two groups. In order to improve the classification accuracy, four different feature selection methods named mutual information maximization (MIM), conditional mutual information maximization (CMIM), fast correlation based filter (FCBF), and double input symmetrical relevance (DISR) are applied to select the most informative features. Multilayer perceptron (MLP) neural network with a hidden layer containing five neurons is used for classification with and without applying the feature selection methods. The accuracy of 82.68, 86.33, 89.67, 84.61, and 91.83 % were observed using entire extracted features and selected features using MIM, CMIM, FCBF, and DISR methods by MLP, respectively. Therefore, the proposed method can be used in clinical setting for more validation.

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Electroencephalography (EEG) is an essential tool ASDs, the exact frequency of electroencephalogram (EEG) for the evaluation and treatment of neurophysiologic disorders. abnormalities in an ASD population that has not had clinical Careful analysis of the EEG records can provide insight and seizures or prior abnormal EEGs is unknown [6].improved understanding of the mechanism causing disorders. The EEG is a record of a time series of potentials caused byIn this study we have investigated the EEG background activity systematic neural activities in brain. The measurements of in patients with Autism disease using frequency analysis the human EEG signals are performed through electrodes methods. We calculated LZ complexity, Short Time Fourier Transform (STFT) and also STFT at bandwith of total placed on the scalp, and they are usually recorded on paper spectrum (we name it STFT BW) for 19 channels of EEG. against time. The voltage of the BEG signal corresponds to Coefficients of the EEG in 11 Autism patients and 10 age itseamplitude. The al its ore calp I l control subjects with the same age were measured. These between 10 and 100 jv, and in adults more commonly 10 coefficient assessments with variance analysis. We find no and 50,uv [7,8]. significant different between Autism disorders and control The application of the nonlinear dynamics (ND) in the BEG subjects EEGs with STFT. On the other hand, Autism alys as recenly nevo and achi(N D th e disorders had significantly difference LZ complexity value analysis has recently developed and achieved some (p<0.05) at electrodes F7, T3 and T5. STFT at bandwidth successes. According to the ND theory, BEGs are nonlinear (STFT_BW) had excellent different. FP1, F3 and T5 with time series produced by the brain and exhibit complex (p<0.01) and F7, T3 and 01 with (p<0.05) had significantly behavior. Nonlinear parameters, such as the correlation differences. In addition our findings suggested that STFT_BW dimension (D2), the approximate entropy (ApEn), the largest have 81.0% discriminate between normal and Autism subjects Lyapunov exponent (LI), etc., characterize the complexity of with Mahalanobis distance but LZ complexity and STFT ND systems. Most of these ND studies can be divided into haven't results significant. two categories: physiological studies and pathological