Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG

Moghaddari, Majid; Lighvan, ‪Mina Zolfy; Daneshvar, Sabalan

doi:10.1016/j.cmpb.2020.105738

Cited by 82 publications

(29 citation statements)

References 35 publications

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“…Similarly, using the ABIDE dataset, which was based on rs-fMRI and deep neural network, ASD was distinguished from typically developing subjects [71]. Wireless Communications and Mobile Computing In addition, it was found that CNN algorithm was most efficient among all applied ML algorithms, and several studies have reported the rise in accuracy for ADHD diagnosis and examination by utilizing CNN with an accuracy range of between 90 ± 10 percent [55,[72][73][74][75][76][77][78]. Similarly, numerous studies were also conducted using CNN for ASD diagnosis and analyses showing a high accuracy rate > 70-90% [44,[79][80][81][82].…”

Section: Recent Machine Learning and Deep Learning Softwarementioning

confidence: 99%

Investigation of Machine Learning Methods for Early Prediction of Neurodevelopmental Disorders in Children

Alam

Raja

Gulzar

2022

Wireless Communications and Mobile Computing

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Several variables, for instance, inheritance and surroundings, influence the growth of neurodevelopmental disorders, e.g., autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) during the first 36 months of life. ADHD and ASD diagnosis mainly rely heavily on traditional clinical assessments from the last few decades. These traditional methods are based on massive data collection from multiple respondents’ responses and the extent of various behavioral descriptors, which are then recognized by the researcher while forming a diagnostic criterion. However, opting for traditional diagnostic methods, there is a high possibility of being misdiagnosed, which may lead to the administration of unnecessary long-term pharmaceutical treatment. That may lead to reduction in functioning and an increase in the risk of developing additional social and clinical issues. Moreover, such diagnostic procedures are also time-consuming and costly. In this sense, rapid and advanced criteria are required to be accurate and cost-effective. Consequently, this study emphasizes the application of machine learning (ML) tools and deep learning (DL) techniques such as convolutional neural network (CNN) and Deep Learning APIs (Application Programming Interface), for the early diagnosis and treatment of ADHD and ASD symptoms. From this investigation, it can be concluded that diagnostic techniques based on ML reduce the intervention time and increase the accuracy with simultaneous understanding of the techniques and algorithms applied to different types of Image data. Numerous studies have been done on ASD and ADHD separately, but our investigation also focuses on cooccurrences of these disorders in one individual.

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Section: Recent Machine Learning and Deep Learning Softwarementioning

confidence: 99%

Investigation of Machine Learning Methods for Early Prediction of Neurodevelopmental Disorders in Children

Alam

Raja

Gulzar

2022

Wireless Communications and Mobile Computing

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“…In the review update in 2020 by Clark et al [42], a number of studies have examined the link between resting EEG and cognitive or CPT performance in ADHD [30], [43]. Further, recent works on ADHD diagnosis by use of machine learning, deep learning and artificial intelligence concepts/technique [44]- [46] suggest that these approach may not only be relevant for clinical applications, but also in planning of targeted treatments for children with ADHD. However, to the best of our knowledge, there are very few EEG studies focused on preschoolers and the investigation of task-related brain dynamics for varying task rates.…”

Section: Related Workmentioning

confidence: 99%

Neural dynamics for facilitating ADHD diagnosis in preschoolers: central and parietal delta synchronization in the Kiddie Continuous Performance Test (Preprint)

Chen¹,

Chang²,

Chang³

et al. 2020

Preprint

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BACKGROUND The present study aimed to characterize children at risk of attention-deﬁcit/hyperactivity disorder (ADHD) at preschool age and provide early intervention. The continuous performance test (CPT) and quantitative electroencephalography (QEEG) can supplement valuable information to facilitate diagnosis. OBJECTIVE This study measured brain dynamics at varying task rates in the CPT using a wireless wearable EEG and identified correlations between the QEEG and CPT data in preschool children with ADHD. METHODS Forty-nine preschool children participated in this study, of which 29 were diagnosed with ADHD and 20 exhibited typical development (TD). Conners Kiddie Continuous Performance Test (K-CPT) and wireless wearable EEG recordings were employed. RESULTS Significant differences were observed between the groups with ADHD and TD in task-related QEEG spectral powers (central delta, as well as posterior delta and beta , P < .01), which were distinct only in the slow-rate condition. A from resting to the CPT task condition induced a central posterior alpha power decrease in the ADHD group. In both resting and task conditions, the delta and theta power were positively correlated with the CPT perseveration scores, whereas the alpha and beta powers were negatively correlated with specific CPT scores mainly on perseveration and omission (P < . 01). CONCLUSIONS These results, which complement the findings of several other studies that have investigated within-task-related brain dynamics in preschoolers, can help specialists working on early intervention to plan training and educational programs for preschoolers with ADHD.

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“…For most of the ADHD detection, the authors extracted nonlinear features and classified with standard classifiers such as support vector machine (SVM), multilayer perceptron, and KNN [26,28,29,34]. A deep convolutional neural networks and deep learning networks were experimented to diagnose ADHD in adults and children [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of ADHD from EEG Signals Using Tunable Q-Factor Wavelet Transform

et al. 2022

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The automatic identification of Attention Deficit Hyperactivity Disorder (ADHD) is essential for developing ADHD diagnosis tools that assist healthcare professionals. Recently, there has been a lot of interest in ADHD detection from EEG signals because it seemed to be a rapid method for identifying and treating this disorder. This paper proposes a technique for detecting ADHD from EEG signals with the nonlinear features extracted using tunable Q-wavelet transform (TQWT). The 16 channels of EEG signal data are decomposed into the optimal amount of time-frequency sub-bands using the TQWT filter banks. The unique feature vectors are evaluated using Katz and Higuchi nonlinear fractal dimension methods at each decomposed levels. An Artificial Neural Network classifier with a 10-fold cross-validation method is found to be an effective classifier for discriminating ADHD and normal subjects. Different performance metrics reveal that the proposed technique could effectively classify the ADHD and normal subjects with the highest accuracy. The statistical analysis showed that the Katz and Higuchi nonlinear feature estimation methods provide potential features that can be classified with high accuracy, sensitivity, and specificity and is suitable for automatic detection of ADHD. The proposed system is capable of accurately distinguishing between ADHD and non-ADHD subjects with a maximum accuracy of 100%.

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Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG

Cited by 82 publications

References 35 publications

Investigation of Machine Learning Methods for Early Prediction of Neurodevelopmental Disorders in Children

Investigation of Machine Learning Methods for Early Prediction of Neurodevelopmental Disorders in Children

Neural dynamics for facilitating ADHD diagnosis in preschoolers: central and parietal delta synchronization in the Kiddie Continuous Performance Test (Preprint)

Detection and Classification of ADHD from EEG Signals Using Tunable Q-Factor Wavelet Transform

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