Assessment of child psychiatric disorders

Bruce, Helen; Evans, Navina

doi:10.1016/j.mppsy.2008.04.008

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Some researchers have used multiple sources to gather the comprehensive information. [1718] Some of these sources were questionnaires, interviews with families, parents and child, and clinical observation. However, they had considered much information simultaneously, which took a long time and also caused diagnostic errors.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Automated and ERP-Based Diagnosis of Attention-Deficit Hyperactivity Disorder in Children

et al. 2017

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Event-related potential (ERP) is one of the most informative and dynamic methods of monitoring cognitive processes, which is widely used in clinical research to deal with a variety of psychiatric and neurological disorders such as attention-deficit/hyperactivity disorder (ADHD). In this study, there were 60 participants including 30 patients with ADHD and 30 subjects as a control group. Their ERP signals were recorded by three electrodes in two modalities. After a preprocessing step, several features such as band power, fractal dimension, autoregressive (AR) model coefficients and wavelet coefficients were extracted from recorded signals. The aim of this study is to achieve a high classification rate. The results show that the fractal dimension–wavelet combination features provided a good discriminative capability; it should be noted that this improvement was achieved by combining all sets of features and applying a feature selection algorithm, which resulted in a maximum accuracy rate of 88.77 and 95.39% in support vector machine (SVM) and v_SVM classification algorithms using a 10-fold cross-validation approach, respectively. ERP has been widely used for clinical diagnosis and cognitive processing deficits in children with ADHD. To increase the accuracy of the diagnostic process of ADHD, ERP signals were recorded to extract some specific ERP features related to this disease for classifying the two groups. The results show that the Fra-wave characterization produced the best average accuracy with an efficiency of 99.43% for v_SVM classifier, compared with 97.65% efficiency for the wavelet features and the other features.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Automated and ERP-Based Diagnosis of Attention-Deficit Hyperactivity Disorder in Children

et al. 2017

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“…Many studies have tried to identify above-mentioned behavioral disorders among children by using different diagnostic methods and tools. Some researchers used multiple sources to gather the comprehensive information [5,6]. Some of these sources are questionnaires, interviews with family, parents and child and clinical observation.…”

Section: Discussionmentioning

confidence: 99%

“…Some researchers suggested that assessment of children with psychiatric disorders should be divided into different stages such as family interview, parental interview, interview with the child, obtaining other sources of information, and physical examination [5,6]. These stages could help obtaining comprehensive information about a child's behavior.…”

Section: Introductionmentioning

confidence: 99%

Designing a Decision Support System for Distinguishing ADHD from Similar Children Behavioral Disorders

et al. 2010

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In this study, a decision support system was designed to distinguish children with ADHD from other similar children behavioral disorders such as depression, anxiety, comorbid depression and anxiety and conduct disorder based on the signs and symptoms. Accuracy of classifying with Radial basis function and multilayer neural networks were compared. Finally, the average accuracy of the networks in classification reached to 95.50% and 96.62% by multilayer and radial basis function networks respectively. Our results indicate that a decision support system, especially RBF, may be a good preliminary assistant for psychiatrists in diagnosing high risk behavioral disorders of children.

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