Assessment of ADHD Through Electroencephalographic Measures of Functional Connectivity

González, José M.; Alba, Guzmán; Sangworasil, Manas; AlmudenaGonzález,; Pereda, Ernesto

doi:10.5772/60559

Cited by 8 publications

(2 citation statements)

References 71 publications

(121 reference statements)

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“…Various neurophysiological and neuroimaging methods were used to evaluate the differences in brain function of children with ADHD [14][15][16][17][18]. Electroencephalography (EEG) can be used to investigate brain activity with a high temporal resolution, low data recording costs, and a wide range of frequency bands [13,19]. Various research has already been done to discriminate ADHD from healthy children.…”

Section: Introductionmentioning

confidence: 99%

Efficient Feature Selection and Machine Learning Based ADHD Detection Using EEG Signal

Maniruzzaman¹,

Shin²,

Hasan³

et al. 2022

Computers, Materials &Amp; Continua

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Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric and neurobehavioral disorders in children, affecting 11% of children worldwide. This study aimed to propose a machine learning (ML)-based algorithm for discriminating ADHD from healthy children using their electroencephalography (EEG) signals. The study included 61 children with ADHD and 60 healthy children aged 7-12 years. Different morphological and time-domain features were extracted from EEG signals. The t-test (p-value < 0.05) and least absolute shrinkage and selection operator (LASSO) were used to select potential features of children with ADHD and enhance the classification accuracy. The selected potential features were used in four ML-based algorithms, including support vector machine (SVM), k-nearest neighbors, multilayer perceptron (MLP), and logistic regression, to classify ADHD and healthy children. The overall prevalence of boys and girls with ADHD was 48.9% and 56.5%, respectively. The average age of children with ADHD was 9.6±1.8 years. Our results illustrated that the combination of LASSO with SVM classifier achieved the highest accuracy of 94.2%, sensitivity of 93.3%, F1-score of 91.9%, and AUC of 0.964. Our results also illustrated that MLP was the second-best ML-based classifier, which gave 93.4% accuracy, 91.7% sensitivity, 91.1% F1-score, and 0.960 AUC. The findings indicated that the combination of the LASSO-based feature selection method and SVM classifier can be a useful tool for selecting reliable/potential features and classifying ADHD and healthy children. Our proposed ML-based algorithms could be useful for the early diagnosis of children with ADHD.

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Section: Introductionmentioning

confidence: 99%

Efficient Feature Selection and Machine Learning Based ADHD Detection Using EEG Signal

Maniruzzaman¹,

Shin²,

Hasan³

et al. 2022

Computers, Materials &Amp; Continua

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“…Basic research with the Electroencephalogram (EEG) showed that ADHD patients had increased absolute and/or relative delta and theta power as well as decreased absolute and/or relative beta and gamma powers in frontal electrodes as compared to healthy controls ( 11 - 13 ). Most previous studies have reported major differences in brain function in the frontal and prefrontal regions between healthy and ADHD groups.…”

mentioning

confidence: 99%

Analysis of Effective Connectivity Strength in Children with Attention Deficit Hyperactivity Disorder Using Phase Transfer Entropy

Ekhlasi

Nasrabadi

Mohammadi

2021

IJPS

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Objective: This study aimed to investigate differences in brain networks between healthy children and children with attention deficit hyperactivity disorder (ADHD) during an attention test. Method: To fulfill this, we constructed weighted directed graphs based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children with the same age. Nodes of graphs were 19 EEG electrodes, and the edges were phase transfer entropy (PTE) between each pair of electrodes. PTE is a measure for directed connectivity that determines the effective relationship between signals in linear and nonlinear coupling. Connectivity graphs of each sample were constructed using PTE in the five frequency bands as follows: delta, theta, alpha, beta, and gamma. To investigate the differences in connectivity strength of each node after the sparsification process with two values (0.5 and 0.25), the permutation statistical test was used with the statistical significance level of p<0.01. Results: The results indicate stronger inter-regional connectivity in the prefrontal brain regions of the control group compared to the ADHD group. However, the strength of inter-regional connectivity in the central regions of the ADHD group was higher. A comparison of the prefrontal regions between the two groups revealed that the areas of the Fp1 electrode (left prefrontal) in healthy individuals play stronger transmission roles. Conclusion: Our research can provide new insights into the strength and direction of connectivity in ADHD and healthy individuals during an attention task.

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Direction of information flow between brain regions in ADHD and healthy children based on EEG by using directed phase transfer entropy

2021

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Assessment of ADHD Through Electroencephalographic Measures of Functional Connectivity

Cited by 8 publications

References 71 publications

Efficient Feature Selection and Machine Learning Based ADHD Detection Using EEG Signal

Efficient Feature Selection and Machine Learning Based ADHD Detection Using EEG Signal

Analysis of Effective Connectivity Strength in Children with Attention Deficit Hyperactivity Disorder Using Phase Transfer Entropy

Direction of information flow between brain regions in ADHD and healthy children based on EEG by using directed phase transfer entropy

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