Identification of Attention Deficit/Hyperactivity Disorder in Children Using Multiple ERP Features

Li, Wenjie; Zhou, Tiantong; Zou, Ling; Lu, Jieru; Liu, Hui; Wang, Suhong

doi:10.2174/1574893612666171201142836

Cited by 5 publications

(4 citation statements)

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“…The focus of this study was on ERP data because the event-related data are more sensitive and provide better performance than spontaneous EEG data recorded while the participants are at a resting state. Various studies have shown that patients with ADHD display brain alterations using ERP attributes (Müller et al 2019 ; Lenartowicz and Loo 2014 ; Öztoprak et al 2017 ; Li et al 2018b ; Kaur et al 2020 ). However, the pre-processing method may also improve the performance of EEG data.…”

Section: Discussionmentioning

confidence: 99%

Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials

2022

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Accurate diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) is a significant challenge. Misdiagnosis has significant negative medical side effects. Due to the complex nature of this disorder, there is no computational expert system for diagnosis. Recently, automatic diagnosis of ADHD by machine learning analysis of brain signals has received an increased attention. This paper aimed to achieve an accurate model to discriminate between ADHD patients and healthy controls by pattern discovery. Event-Related Potentials (ERP) data were collected from ADHD patients and healthy controls. After pre-processing, ERP signals were decomposed and features were calculated for different frequency bands. The classification was carried out based on each feature using seven machine learning algorithms. Important features were then selected and combined. To find specific patterns for each model, the classification was repeated using the proposed patterns. Results indicated that the combination of complementary features can significantly improve the performance of the predictive models. The newly developed features, defined based on band power, were able to provide the best classification using the Generalized Linear Model, Logistic Regression, and Deep Learning with the average accuracy and Receiver operating characteristic curve > %99.85 and > 0.999, respectively. High and low frequencies (Beta, Delta) performed better than the mid, frequencies in the discrimination of ADHD from control. Altogether, this study developed a machine learning expert system that minimises misdiagnosis of ADHD and is beneficial for the evaluation of treatment efficacy. Graphical abstract

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Section: Discussionmentioning

confidence: 99%

Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials

2022

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“…The author in [20] found that the focal length had the strongest association with reading speed and accuracy among 2679 youngsters (ages 7-9). For 3644 respondents (aged 7-17) in a gamified online test with 32 Spanish language exercises, reading tasks were processed in [21] using a method that eliminated the need for eye movements. A diagnosis of dyslexia was given to 392 participants (10.8%) or people.…”

Section: Related Workmentioning

confidence: 99%

A Linear Regularized Normalized Model for Dyslexia and ADHD Prediction Using Learning Approaches

Pavan Kumar Varma Kothapalli

2023

IJRITCC

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A learning disability called dyslexia typically affects school-age kids. Children have trouble spelling, reading, and writing words. Children who experience this problem often struggle with negative emotions, rage, frustration, and low self-esteem. Consequently, a dyslexia predictor system is required to assist children in overcoming the risk. There are many current ways of predicting dyslexia. However, they need to provide higher prediction accuracy. Also, this work concentrates on another disorder known as Attention-Deficit Hyperactivity Disorder (ADHD). The prediction process is more challenging as the prediction process shows some negative consequences. The data is typically gathered from online resources for the prediction process. This study examines how the predictor model predicts dyslexia and ADHD using learning strategies. Here, the most important features for accurately classifying dyslexia, non-dyslexia and ADHD are extracted using a new Support Vector Machine (SVM) for feature selection based on the norm and norm. Based on the weighted values, the predicted model provides improved subset features from the internet-accessible dataset. The accuracy, precision, F1-score, specificity, sensitivity, and execution time are all examined here using 10-fold cross-validation. The maximum accuracy reached with this feature subset during the prediction process is carefully reviewed. The experiment results imply that the anticipated model is used to accurately predict the defect and as a tool for CDSS. Recently, dyslexia and ADHD prediction has been greatly aided by computer-based predictor systems. The expected model also effectively fits the experimental design, bridging the gap between feature selection and classification.

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“…These developmental problems have been identified by fMRI assessments, as reported by Tang et al [11]. Numerous EEG investigations have shown atypical amplitude patterns in the brain waves of individuals with ADHD [12][13][14]. Individuals diagnosed with ADHD exhibit distinct EEG patterns that indicate deviations in neuropsychological functioning when compared to neurotypical individuals.…”

Section: Introductionmentioning

confidence: 99%

Developing System-Based Artificial Intelligence Models for Detecting the Attention Deficit Hyperactivity Disorder

Alkahtani,

Aldhyani,

Ahmed

et al. 2023

Mathematics

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This study presents a novel methodology for automating the classification of pediatric ADHD using electroencephalogram (EEG) biomarkers through machine learning and deep learning techniques. The primary objective is to develop accurate EEG-based screening tools to aid clinical diagnosis and enable early intervention for ADHD. The proposed system utilizes a publicly available dataset consisting of raw EEG recordings from 61 individuals with ADHD and 60 control subjects during a visual attention task. The methodology involves meticulous preprocessing of raw EEG recordings to isolate brain signals and extract informative features, including time, frequency, and entropy signal characteristics. The feature selection techniques, including least absolute shrinkage and selection operator (LASSO) regularization and recursive elimination, were applied to identify relevant variables and enhance generalization. The obtained features are processed by employing various machine learning and deep learning algorithms, namely CatBoost, Random Forest Decision Trees, Convolutional Neural Networks (CNNs), and Long Short-Term Memory Networks (LSTMs). The empirical results of the proposed algorithms highlight the effectiveness of feature selection approaches in matching informative biomarkers with optimal model classes. The convolutional neural network model achieves superior testing accuracy of 97.75% using LASSO-regularized biomarkers, underscoring the strengths of deep learning and customized feature optimization. The proposed framework advances EEG analysis to uncover discriminative patterns, significantly contributing to the field of ADHD screening and diagnosis. The suggested methodology achieved high performance compared with different existing systems based on AI approaches for diagnosing ADHD.

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Identification of Attention Deficit/Hyperactivity Disorder in Children Using Multiple ERP Features

Cited by 5 publications

References 0 publications

Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials

Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials

A Linear Regularized Normalized Model for Dyslexia and ADHD Prediction Using Learning Approaches

Developing System-Based Artificial Intelligence Models for Detecting the Attention Deficit Hyperactivity Disorder

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