Background and Aim: Autism-Spectrum Disorders (ASD) are neural connectivity abnormalities at global and local of brain levels. A one-dimensional non-invasive technique that allows a highly accurate measurement of brain function and connectivity is Quantitative electroencephalography (QEEG). This is a systemic review that encompasses the key finding of QEEG application in subjects with ASD, in order to assess the relevance of this approach in characterizing brain function and clustering phenotypes.

Materials and Methods: QEEG studies evaluating both the spontaneous brain activity and brain signals under controlled experimental stimuli were examined. In despite of conflicting results, literature analysis suggests that QEEG features are sensitive to modification in neuronal regulation dysfunctions which characterizes autistic brain but features types are very important.

Results: Therefore QEEG may help in detecting regions of altered brain function and connectivity abnormalities, in linking behavior with brain activity, and subgrouping affected individuals within the wide heterogeneity of ASD. The use of advanced techniques for the increase of the specificity and of spatial localization could allow finding distinctive patterns of QEEG abnormalities in ASD subjects, paving the way for the development of tailored intervention strategies.

Conclusion: Autism is a disorder in which multiple aspects of behavior, emotion, language and cognition are disrupted, among which, autistic individuals appear to have a range of perceptual processing abnormalities, expressed especially entirely by a high level of sensitivity to auditory and tactile stimuli. It seems Autism Spectrum Disorder (ASD) is potentially caused by unbalanced portion of excitation/inhibition, in other words a disproportionate high level of excitation (or disproportionately weak inhibition) in neural circuits that mediate language and social behaviors. Holistic approaches could help us to have better detections

Keywords: Diagnosis autism-spectrum disorder, Quantitative electroencephalography, Coherence, asymmetry, Non-linear techniques

Background and Aim: Knee joint injuries are the most common injuries in routine life and stirring sports. The most common injuries in knee joint are meniscus injuries, anterior cruciate ligament rupture and kind of tears of above structures. Diagnosis of meniscus tear is generally clinically and by magnetic resonance imaging (MRI: Magnetic Resonance Imaging). In this study, meniscuc tear was recognized by recorded the knee vibration signals (VAG: Vibroarthrography).

Materials and Methods: Forty subjects (20 normal and 20 abnormal) with meniscus tear were selected and recorded the signals by electrostethoscope, 3 times in 15 sec. Testimonial form was taken from all of participants. 

Results: After recording, the signals were processed and reduced the noise by singular value decomposition algorithm (SVD: Singular Value Decomposition), four parameters of these signals were extracted in energy and frequency domain. These were included energy parameter (EP: Energy Parameter), energy spread parameter (ESP: Energy Spread Prameter), frequency parameter (FP: Frequency Parameter) and frequency spread parameter (FSP: Frequency Spread Parameter). Mean and standard deviation of each feature were considered and analyzed eight features of the signals. Statistical analyzes showed the P-Value less than 0.05 ( ) for each feature. Three methods for data classification were used. ) Mean and standard deviation of the parameters were obtained as below: Multi Layer Perceptron (MLP: Multi Layer Perceptron), Support Vector Machine (SVM: Support Vector Machine) and K- Nearest Neighbor (KNN: K-Nearest Neighbor) with ( ), ( ) and ( . K-nearest neighbor method (K=5) has the highest percentage of accuracy.

Conclusion: Knee signals processing (VAG signals) is a suitable and non-invasive method for diagnosis of meniscus tear which can save the time and reduce the costs.

Keywords: Articular pathology, Meniscus tear, VAG signals, Singular Value Decomposition (SVD), Time- frequency distribution.