Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Behnaz Sheikholeslami; Ghasem Sadeghi Bajestani; Reza Yaghoobi Karimui; Reyhaneh Zarifiyan
Volume 15, Issue 1 , May 2021, , Pages 29-46
Abstract
Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that can affect people of all ages in the community, especially children, and cause changes in their behavior. Previous studies have often focused on frequency domain processing or the nonlinear dynamic aspects of EEG signals ...
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Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that can affect people of all ages in the community, especially children, and cause changes in their behavior. Previous studies have often focused on frequency domain processing or the nonlinear dynamic aspects of EEG signals such as correlation dimension, fractal dimension, Lyapunov exponent, entropy, and recurrence rate of brain processes to differentiate individuals with ADHD. In this study, we evaluate the volume of the EEG signal oscillation basin using Poincare sections in the phase space of EEG signals of people with ADHD and healthy people and sort this space as well as extract various geometric features. We present a different perspective of complexity of brain activity and the level of dynamism of people with ADHD compared to healthy individuals. Finally, by evaluating the extracted features and using the SFS algorithm based on the RBF-SVM classifier, we were able to separate people with ADHD from healthy people in the groups of children and adults, with accuracy of 93.20±2.04 and 95.60±1.13. The results of this study showed that the volume of the EEG signal oscillation basin in people with ADHD was significantly higher than healthy people, which indicates an increase in the degree of dynamism and thus a decrease in the complexity of brain activity in these people. It was also identified in this study that the increase in the volume of the EEG signal oscillation basin in children is more than adults, which indicates an increase in the level of dynamism of children compared to adults. Therefore, ADHD and age can be introduced as two important factors in changing the volume of the EEG signal oscillation basin.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Marjan Mozaffarilegha; Seyed Mohammad Sadegh Movahed
Volume 11, Issue 3 , September 2017, , Pages 255-264
Abstract
The complexities and the effects of inter-subject variations on the encoding of sounds are features of the brainstem processing. Examining such data based on linear analysis is not reliable, encouraging to take into account non-linear methods which are effective ways of explaining such non-stationary ...
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The complexities and the effects of inter-subject variations on the encoding of sounds are features of the brainstem processing. Examining such data based on linear analysis is not reliable, encouraging to take into account non-linear methods which are effective ways of explaining such non-stationary signals. The purpose of this study is to explore the behavior of the brainstem in response to complex auditory stimuli /da/ using Multifractal Detrended Fluctuation Analysis modified by Singular Value Decomposition (SVD), Adaptive Detrending (AD) and Empirical Mode Decomposition (EMD). Auditory brainstem responses to synthetic /da/ stimuli were recorded for 40 normal subjects with a mean age of 22.7 years. MFDFA is carried out on the s-ABR time series data to evaluate the variation of their complexity and multiscaling. To utilize optimal Detrending of s-ABR time series, AD, SVD and EMD algorithms are applied on time series. By computing the fluctuation function and evaluating scaling behavior, scaling exponents such as generalized Hurst exponent and multifractal spectrum are determined. Given results in this method indicate that underlying signal has non-stationary nature in small scales, but property of system is controlled by trend in large scales. There is a crossover at msec on the behavior of fluctuation function corresponding to dominant sinusoidal trend in all samples. The average of Hurst exponent is at 68% confidence interval in small scales msec. The -dependency of demonstrate that underlying data sets have multifractality nature and are almost due to long-range correlations. The width of singularity spectrum which is a measure of the signal complexity of underlying data in average equates to at confidence interval.