Classification Of Attention Deficit Hyperactivity Disorder Research Articles

A novel approach to ADHD classification based on severity and emotional impairment: Findings from artificial intelligence analysis

Attention Deficit Hyperactivity Disorder (ADHD) is a disorder characterized by symptoms of inattention and executive dysfunction, although there is not always agreement on the onset, course and long-term stability of the diagnosis. This study aims to detect differences in the cognitive profile according to the subtype of ADHD following a professional diagnosis and to propose an alternative classification. The scores obtained for each cognitive construct were compared using the Student’s t-test. In order to explore different diagnostic categories based on groupings made by Artificial Intelligence (AI) subjects were grouped based on their performance through the K-means clustering technique. The results obtained by Artificial Intelligence (AI) identified groups based on the severity of the cognitive profile and the presence of emotional impairment. Difficulties in perceived planning within family and school environments were highlighted as major risk factors in the severity of ADHD in children. Emotional disturbances perceived by both parents, such as depressive symptoms, anxiety, and somatization, were observed subsequently. In accordance with the results, an alternative way to classify ADHD is possible, involving categorization according to the presence or absence of emotional impairment, along with the severity of impairment in attentional and executive functions.

Enhanced ADHD classification through deep learning and dynamic resting state fMRI analysis.

Attention Deficit Hyperactivity Disorder (ADHD) is characterized by deficits in attention, hyperactivity, and/or impulsivity. Resting-state functional connectivity analysis has emerged as a promising approach for ADHD classification using resting-state functional magnetic resonance imaging (rs-fMRI), although with limited accuracy. Recent studies have highlighted dynamic changes in functional connectivity patterns among ADHD children. In this study, we introduce Skip-Vote-Net, a novel deep learning-based network designed for classifying ADHD from typically developing children (TDC) by leveraging dynamic connectivity analysis on rs-fMRI data collected from 222 participants included in the NYU dataset within the ADHD-200 database. Initially, for each subject, functional connectivity matrices were constructed from overlapping segments using Pearson's correlation between mean time series of 116 regions of interest defined by the Automated Anatomical Labeling (AAL) 116 atlas. Skip-Vote-Net was then developed, employing a majority voting mechanism to classify ADHD/TDC children, as well as distinguishing between the two main subtypes: the inattentive subtype (ADHDI) and the predominantly combined subtype (ADHDC). The proposed method was evaluated across four classification scenarios: (1) two-class classification of ADHD from TD children using balanced data, (2) two-class classification between ADHD and TD children using unbalanced data, (3) two-class classification between ADHDI and ADHDC, and (4) three-class classification among ADHDI, ADHDC, and TD children. Using Skip-Vote-Net, we achieved mean classification accuracies of 97% ± 1.87 and 97.7% ± 2.2 for the balanced and unbalanced classification cases, respectively. Furthermore, the mean classification accuracy for discriminating between ADHDI and ADHDC reached 99.4% ± 1.21. Finally, the proposed method demonstrated an average accuracy of 98.86% ± 1.03 in classifying ADHDI, ADHDC, and TD children collectively. Our findings highlight the superior performance of Skip-Vote-Net over existing methods in the classification of ADHD, showcasing its potential as an effective diagnostic tool for identifying ADHD subtypes and distinguishing ADHD from typically developing children.

Role of personal characteristics data for classification of attention-deficit hyperactivity disorder

Diagnosed in millions of children, ADHD is the leading mental health concern in childhood. Several steps and a lot of personal characteristics (PC) are required from various sources for accurate analysis of ADHD and its subtype (Hyperactive (ADHD-H), Combined (ADHD-C), or Inattentive (ADHD-I)). Moreover, there is no standard automatic diagnostic tool to differentiate ADHD, its subtype, and typical developing (TD) using PC data. The present work focused on the development of a machine learning-based automatic diagnostic tool for the classification of TD, ADHD, and its subtypes using PC data that can be helpful for clinicians. In this work, eight datasets (D1 to D8, four balanced and four unbalanced) are constructed from publicly available dataset and three sets of features were built. Five popular classifiers, namely K-Nearest Neighbor (KNN), Logistic Regression Classifier (LRC), Random Forest (RF), Support Vector Machine (SVM), and Radial Basis Function Support Vector Machine (RBSVM), were trained for the datasets. To comprehensively evaluate performance, the evaluation involved ten iterations of a 10-fold cross-validation approach to calculate average classification accuracy, recall, specificity, and F1 score. Gender, IQMeasure, Full4IQ, and Handedness are observed to be relevant for the classification. Overall, it is observed that RBSVM outperformed other classifiers in most cases.

Convolutional neural network framework for EEG-based ADHD diagnosis in children.

Attention-deficit hyperactivity disorder (ADHD) stands as a significant psychiatric and neuro-developmental disorder with global prevalence. The prevalence of ADHD among school children in India is estimated to range from 5% to 8%. However, certain studies have reported higher prevalence rates, reaching as high as 11%. Utilizing electroencephalography (EEG) signals for the early detection and classification of ADHD in children is crucial. In this study, we introduce a CNN architecture characterized by its simplicity, comprising solely two convolutional layers. Our approach involves pre-processing EEG signals through a band-pass filter and segmenting them into 5-s frames. Following this, the frames undergo normalization and canonical correlation analysis. Subsequently, the proposed CNN architecture is employed for training and testing purposes. Our methodology yields remarkable results, with 100% accuracy, sensitivity, and specificity when utilizing the complete 19-channel EEG signals for diagnosing ADHD in children. However, employing the entire set of EEG channels presents challenges related to the computational complexity. Therefore, we investigate the feasibility of using only frontal brain EEG channels for ADHD detection, which yields an accuracy of 99.08%. The proposed method yields high accuracy and is easy to implement, hence, it has the potential for widespread practical deployment to diagnose ADHD.

ADHD classification with cross-dataset feature selection for biomarker consistency detection

Objective. Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children. While numerous intelligent methods are applied for its subjective diagnosis, they seldom consider the consistency problem of ADHD biomarkers. In practice, these data-driven approaches lead to varying learned features for ADHD classification across diverse ADHD datasets. This phenomenon significantly undermines the reliability of identified biomarkers and hampers the interpretability of these methods. Approach. In this study, we propose a cross-dataset feature selection (FS) module using a grouped SVM-based recursive feature elimination approach (G-SVM-RFE) to enhance biomarker consistency across multiple datasets. Additionally, we employ connectome gradient data for ADHD classification. In details, we introduce the G-SVM-RFE method to effectively concentrate gradient components within a few brain regions, thereby increasing the likelihood of identifying these regions as ADHD biomarkers. The cross-dataset FS module is integrated into an existing binary hypothesis testing (BHT) framework. This module utilizes external datasets to identify global regions that yield stable biomarkers. Meanwhile, given a dataset which waits for implementing the classification task as local dataset, we learn its own specific regions to further improve the performance of accuracy on this dataset. Main results. By employing this module, our experiments achieve an average accuracy of 96.7% on diverse datasets. Importantly, the discriminative gradient components primarily originate from the global regions, providing evidence for the significance of these regions. We further identify regions with the high appearance frequencies as biomarkers, where all the used global regions and one local region are recognized. Significance. These biomarkers align with existing research on impaired brain regions in children with ADHD. Thus, our method demonstrates its validity by providing enhanced biological explanations derived from ADHD mechanisms.

ADHD diagnosis guided by functional brain networks combined with domain knowledge

Utilizing functional magnetic resonance imaging (fMRI) to model functional brain networks (FBNs) is increasingly prominent in attention-deficit/hyperactivity disorder (ADHD) research, revealing neural impact and mechanisms through the exploration of activated brain regions. However, current FBNs-based methods face two major challenges. The primary challenge stems from the limitations of existing modeling methods in accurately capturing both regional correlations and long-distance dependencies (LDDs) within the dynamic brain, thereby affecting the diagnostic accuracy of FBNs as biomarkers. Additionally, limited sample size and class imbalance also pose a challenge to the learning performance of the model. To address the issues, we propose an automated diagnostic framework, which integrates modeling, multimodal fusion, and classification into a unified process. It aims to extract representative FBNs and efficiently incorporate domain knowledge to guide ADHD classification. Our work mainly includes three-fold: 1) A multi-head attention-based region-enhancement module (MAREM) is designed to simultaneously capture regional correlations and LDDs across the entire sequence of brain activity, which facilitates the construction of representative FBNs. 2) The multimodal supplementary learning module (MSLM) is proposed to integrate domain knowledge from phenotype data with FBNs from neuroimaging data, achieving information complementarity and alleviating the problems of insufficient medical data and unbalanced sample categories. 3) An ADHD automatic diagnosis framework guided by FBNs and domain knowledge (ADF-FAD) is proposed to help doctors make more accurate decisions, which is applied to the ADHD-200 dataset to confirm its effectiveness. The results indicate that the FBNs extracted by MAREM perform well in modeling and classification. After with MSLM, the model achieves accuracy of 92.4%, 74.4%, and 80% at NYU, PU, and KKI, respectively, demonstrating its ability to effectively capture crucial information related to ADHD diagnosis. Codes are available at https://github.com/zhuimengxuebao/ADF-FAD.

Detection and Classification of ADHD Using Deep Learning Based on EEG Signals

Attention Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder, is gaining increasing public and academic attention. To illuminate the current state of ADHD research, this paper reviews a range of studies, including but not limited to epidemiology, pathological origins, patient challenges, and mainstream treatments. It also analyzes a series of experiments and studies that use automated tools to analyze physiological signals for ADHD detection and differentiation. The first section provides a comprehensive summary of existing research on the pathological origins and subtype differences of ADHD, covering multiple perspectives and levels. This lays a theoretical foundation for the idea of analyzing ADHD using EEG signals. The second section focuses on distinctions in EEG signals among individuals with normal neurodevelopment, ADHD populations, and its subtypes, demonstrating that deep learning can be used to train on such physiological signals for automated diagnosis. The third section highlights existing studies that leverage deep learning, outlining the potential developments in this field. The paper objectively analyzes and summarizes obstacles and issues that may be encountered on the research path of utilizing deep learning as a tool for automated ADHD diagnosis, offering recommendations for future development. Through this review, the progress and limitations in ADHD research become clear, providing insights for continued and more informed development in the field.

Temporal Alpha Dissimilarity of ADHD Brain Network in Comparison With CPT and CATA.

Attention deficit hyperactivity disorder (ADHD) is a chronic neurological and psychiatric disorder that affects children during their development. To find neural patterns for ADHD and provide subjective features as decision references to assist specialists and physicians. Many studies have been devoted to investigating the neural dynamics of the brain through resting-state or continuous performance tests (CPT) with EEG or functional magnetic resonance imaging (fMRI). The present study used coherence, which is one of the functional connectivity (FC) methods, to analyze the neural patterns of children and adolescents (8-16 years old) under CPT and continuous auditory test of attention (CATA) task. In the meantime, electroencephalography (EEG) oscillations were recorded by a wireless brain-computer interface (BCI). 72 children were enrolled, of which 53 participants were diagnosed with ADHD and 19 presented to be typical developing (TD). The experimental results exhibited a higher difference in alpha and theta bands between the TD group and the ADHD group. While the differences between the TD group and the ADHD group in all four frequency domains were greater than under CPT conditions. Statistically significant differences ( [Formula: see text]) were observed between the ADHD and TD groups in the alpha rhythm during the CATA task in the short-range of coherence. For the temporal lobe FC during the CATA task, the TD group exhibited statistically significantly FC ( [Formula: see text]) in the alpha rhythm compared to the ADHD group. These findings offering new possibilities for more techniques and diagnostic methods in finding more ADHD features. The differences in alpha and beta frequencies were more pronounced in the ADHD group during the CPT task compared to the CATA task. Additionally, the disparities in brain activity were more evident across delta, theta, alpha and beta frequency domains when the task given was a CATA as opposed to a CPT. The findings presented the underlying mechanisms of the FC differences between children and adolescents with ADHD. Moreover, these findings should extend to use machine learning approaches to assist the ADHD classification and diagnosis.

Adaptive spatial-temporal neural network for ADHD identification using functional fMRI.

Computer aided diagnosis methods play an important role in Attention Deficit Hyperactivity Disorder (ADHD) identification. Dynamic functional connectivity (dFC) analysis has been widely used for ADHD diagnosis based on resting-state functional magnetic resonance imaging (rs-fMRI), which can help capture abnormalities of brain activity. However, most existing dFC-based methods only focus on dependencies between two adjacent timestamps, ignoring global dynamic evolution patterns. Furthermore, the majority of these methods fail to adaptively learn dFCs. In this paper, we propose an adaptive spatial-temporal neural network (ASTNet) comprising three modules for ADHD identification based on rs-fMRI time series. Specifically, we first partition rs-fMRI time series into multiple segments using non-overlapping sliding windows. Then, adaptive functional connectivity generation (AFCG) is used to model spatial relationships among regions-of-interest (ROIs) with adaptive dFCs as input. Finally, we employ a temporal dependency mining (TDM) module which combines local and global branches to capture global temporal dependencies from the spatially-dependent pattern sequences. Experimental results on the ADHD-200 dataset demonstrate the superiority of the proposed ASTNet over competing approaches in automated ADHD classification.

Acoustic and Text Features Analysis for Adult ADHD Screening: A Data-Driven Approach Utilizing DIVA Interview.

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder commonly seen in childhood that leads to behavioural changes in social development and communication patterns, often continues into undiagnosed adulthood due to a global shortage of psychiatrists, resulting in delayed diagnoses with lasting consequences on individual's well-being and the societal impact. Recently, machine learning methodologies have been incorporated into healthcare systems to facilitate the diagnosis and enhance the potential prediction of treatment outcomes for mental health conditions. In ADHD detection, the previous research focused on utilizing functional magnetic resonance imaging (fMRI) or Electroencephalography (EEG) signals, which require costly equipment and trained personnel for data collection. In recent years, speech and text modalities have garnered increasing attention due to their cost-effectiveness and non-wearable sensing in data collection. In this research, conducted in collaboration with the Cumbria, Northumberland, Tyne and Wear NHS Foundation Trust, we gathered audio data from both ADHD patients and normal controls based on the clinically popular Diagnostic Interview for ADHD in adults (DIVA). Subsequently, we transformed the speech data into text modalities through the utilization of the Google Cloud Speech API. We extracted both acoustic and text features from the data, encompassing traditional acoustic features (e.g., MFCC), specialized feature sets (e.g., eGeMAPS), as well as deep-learned linguistic and semantic features derived from pre-trained deep learning models. These features are employed in conjunction with a support vector machine for ADHD classification, yielding promising outcomes in the utilization of audio and text data for effective adult ADHD screening. Clinical impact: This research introduces a transformative approach in ADHD diagnosis, employing speech and text analysis to facilitate early and more accessible detection, particularly beneficial in areas with limited psychiatric resources. Clinical and Translational Impact Statement: The successful application of machine learning techniques in analyzing audio and text data for ADHD screening represents a significant advancement in mental health diagnostics, paving the way for its integration into clinical settings and potentially improving patient outcomes on a broader scale.

Functional brain network identification and fMRI augmentation using a VAE-GAN framework

Recently, deep learning models have achieved superior performance for mapping functional brain networks from functional magnetic resonance imaging (fMRI) data compared with traditional methods. However, due to the lack of sufficient data and the high dimensionality of brain volume, deep learning models of fMRI tend to suffer from overfitting. In addition, existing methods rarely studied fMRI data augmentation and its application. To address these issues, we developed a VAE-GAN framework that combined a VAE (variational auto-encoder) with a GAN (generative adversarial net) for functional brain network identification and fMRI augmentation. As a generative model, the VAE-GAN models the distribution of fMRI so that it enables the extraction of more generalized features, and thus relieve the overfitting issue. The VAE-GAN is easier to train on fMRI than a standard GAN since it uses latent variables from VAE to generate fake data rather than relying on random noise that is used in a GAN, and it can generate higher quality of fake data than VAE since the discriminator can promote the training of the generator. In other words, the VAE-GAN inherits the advantages of VAE and GAN and avoids their limitations in modeling of fMRI data. Extensive experiments on task fMRI datasets from HCP have proved the effectiveness and superiority of the proposed VAE-GAN framework for identifying both temporal features and functional brain networks compared with existing models, and the quality of fake data is higher than those from VAE and GAN. The results on resting state fMRI of Attention Deficit Hyperactivity Disorder (ADHD)-200 dataset further demonstrated that the fake data generated by the VAE-GAN can help improve the performance of brain network modeling and ADHD classification.

ADHD diagnosis using structural brain MRI and personal characteristic data with machine learning framework.

An essential yet challenging task is an automatic diagnosis of attention-deficit/hyperactivity disorder (ADHD) without manual intervention. The present study emphasises utilizing structural MRI and personal characteristic (PC) data for developing an automated diagnostic system for ADHD classification. Here, an age-balanced dataset of 316 ADHD and 316 Typically Developing Children (TDC) was prepared from the publicly available dataset. We extracted volumetric features from gray matter (GM) volumes from brain regions defined by Automated Anatomical Labelling (AAL3) atlas and cortical thickness-based (CT) features using the Destrieux atlas. A set of salient features were selected independently using minimum redundancy and maximum relevance (mRMR) and ensemble feature selection (EFS) methods. Decision models were trained using five well-known classifiers: K-nearest neighbours, logistic regression, linear Support Vector Machine (SVM), radial-based SVM (RBSVM), and Random Forest. The performance of the proposed system was evaluated using accuracy, recall, and specificity with ten runs of a ten-fold cross-validation scheme. We run seven experiments by considering different combinations of features. The maximum classification accuracy of 75% was obtained with CT and PC features with RBSVM and SVM with the EFS. An increase in GM volume in fifteen brain regions and loss of cortical thickness in twenty-seven brain regions were observed.

ADHD classification combining biomarker detection with attention auto-encoding neural network

Attention Deficit Hyperactivity Disorder (ADHD) is one of most prevalent neurodevelopmental disorders in children. In decades, various neurobiological diagnosis methods have been well developed, yielding ADHD classification accuracy significantly improved. To our knowledge, deep-learning-based approaches attract more interest nowadays, since they effectively learn the potential features indicating ADHD disease. However, these learned features cannot directly project to certain biomarkers that have neurobiological meanings, which greatly hinders the neurobiological findings on ADHD. To ameliorate this problem, we proposed an attention auto-encoding network (Att-AENet) under an existing binary hypothesis testing (BHT) framework. By using brain functional connectivities (FCs) as input materials, an attention mechanism was employed to calculate the weights of FCs and measure the FC significance on ADHD. Therefore, the FC biomarkers of ADHD were conveniently identified from these weights. In detail, we built two attention subnetworks with dense and two-dimension convolution structures, respectively, and then evaluated the effect of these subnetworks from the viewpoint of ADHD classification. Sequentially, we took the biomarker detection analysis on the proposed dense-based attention subnetwork for its higher classification accuracy. On the test of ADHD-200 database, it shows the Att-AENet achieves a remarkable classification result with the average accuracy of 98.9%, especially 99.9% on some large-size datasets. More importantly, the FC biomarkers, corresponding to the discriminative weights learned by our attention subnetwork, are in accord with existing reported results. It demonstrates the reliability of these biomarkers. As a result, the proposed Att-AENet well fulfills the task of ADHD biomarker detection.

Attention Deficit Hyperactivity Disorder Classification Based on Deep Learning.

Attention Deficit Hyperactivity Disorder (ADHD) is a type of mental health disorder that can be seen from children to adults and affects patients' normal life. Accurate diagnosis of ADHD as early as possible is very important for the treatment of patients in clinical applications. Some traditional classification methods, although having been shown powerful in many other classification tasks, are not as successful in the application of ADHD classification. In this paper, we propose two novel deep learning approaches for ADHD classification based on functional magnetic resonance imaging. The first method incorporates independent component analysis with convolutional neural network. It first extracts independent components from each subject. The independent components are then fed into a convolutional neural network as input features to classify the ADHD patient from typical controls. The second method, called the correlation autoencoder method, uses correlations between regions of interest of the brain as the input of an autoencoder to learn latent features, which are then used in the classification task by a new neural network. These two methods use different ways to extract the inter-voxel information from fMRI, but both use convolutional neural networks to further extract predictive features for the classification task. Empirical experiments show that both methods are able to outperform the classical methods such as logistic regression, support vector machines, and other methods used in previous studies.

Multiple measurement analysis of resting-state fMRI for ADHD classification in adolescent brain from the ABCD study

Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric disorders in school-aged children. Its accurate diagnosis looks after patients’ interests well with effective treatment, which is important to them and their family. Resting-state functional magnetic resonance imaging (rsfMRI) has been widely used to characterize the abnormal brain function by computing the voxel-wise measures and Pearson’s correlation (PC)-based functional connectivity (FC) for ADHD diagnosis. However, exploring the powerful measures of rsfMRI to improve ADHD diagnosis remains a particular challenge. To this end, this paper proposes an automated ADHD classification framework by fusion of multiple measures of rsfMRI in adolescent brain. First, we extract the voxel-wise measures and ROI-wise time series from the brain regions of rsfMRI after preprocessing. Then, to extract the multiple functional connectivities, we compute the PC-derived FCs including the topographical information-based high-order FC (tHOFC) and dynamics-based high-order FC (dHOFC), the sparse representation (SR)-derived FCs including the group SR (GSR), the strength and similarity guided GSR (SSGSR), and sparse low-rank (SLR). Finally, these measures are combined with multiple kernel learning (MKL) model for ADHD classification. The proposed method is applied to the Adolescent Brain and Cognitive Development (ABCD) dataset. The results show that the FCs of dHOFC and SLR perform better than the others. Fusing multiple measures achieves the best classification performance (AUC = 0.740, accuracy = 0.6916), superior to those from the single measure and the previous studies. We have identified the most discriminative FCs and brain regions for ADHD diagnosis, which are consistent with those of published literature.

Deep-Learning-Based ADHD Classification Using Children's Skeleton Data Acquired through the ADHD Screening Game.

The identification of attention deficit hyperactivity disorder (ADHD) in children, which is increasing every year worldwide, is very important for early diagnosis and treatment. However, since ADHD is not a simple disease that can be diagnosed with a simple test, doctors require a large period of time and substantial effort for accurate diagnosis and treatment. Currently, ADHD classification studies using various datasets and machine learning or deep learning algorithms are actively being conducted for the screening diagnosis of ADHD. However, there has been no study of ADHD classification using only skeleton data. It was hypothesized that the main symptoms of ADHD, such as distraction, hyperactivity, and impulsivity, could be differentiated through skeleton data. Thus, we devised a game system for the screening and diagnosis of children's ADHD and acquired children's skeleton data using five Azure Kinect units equipped with depth sensors, while the game was being played. The game for screening diagnosis involves a robot first travelling on a specific path, after which the child must remember the path the robot took and then follow it. The skeleton data used in this study were divided into two categories: standby data, obtained when a child waits while the robot demonstrates the path; and game data, obtained when a child plays the game. The acquired data were classified using the RNN series of GRU, RNN, and LSTM algorithms; a bidirectional layer; and a weighted cross-entropy loss function. Among these, an LSTM algorithm using a bidirectional layer and a weighted cross-entropy loss function obtained a classification accuracy of 97.82%.

Wavelet-based approach for diagnosing attention deficit hyperactivity disorder (ADHD)

Attention deficit hyperactivity disorder (ADHD) is a common cognitive disorder affecting children. ADHD can interfere with educational, social, and emotional development, so early detection is essential for obtaining proper care. Standard ADHD diagnostic protocols rely heavily on subjective assessments of perceived behavior. An objective diagnostic measure would be a welcome development and potentially aid in accurately and efficiently diagnosing ADHD. Analysis of pupillary dynamics has been proposed as a promising alternative method of detecting affected individuals effectively. This study proposes a method based on the self-similarity of pupillary dynamics and assesses its strength as a potential diagnostic biomarker. Localized discriminatory features are developed in the wavelet domain and selected via a rolling window method to build classifiers. The application on a task-based pupil diameter time series dataset of children aged 10–12 years shows that the proposed method achieves greater than 78% accuracy in detecting ADHD. Comparing with a recent approach that constructs features in the original data domain, the proposed wavelet-based classifier achieves more accurate ADHD classification with fewer features. The findings suggest that the proposed diagnostic procedure involving interpretable wavelet-based self-similarity features of pupil diameter data can potentially aid in improving the efficacy of ADHD diagnosis.

ADHD and reification: Four ways a psychiatric construct is portrayed as a disease.

The descriptive classification Attention-Deficit/Hyperactivity Disorder (ADHD) is often mistaken for a disease entity that explains the causes of inattentive and hyperactive behaviors, rather than merely describing the existence of such behaviors. The present study examines discourse on ADHD to analyze how authors passively and actively contribute to reification-a fallacy in which a concept is represented as a thing existing on its own. Critical Discourse Analysis and Qualitative Content Analysis of academic textbooks, scientific articles, websites and videos were used to analyze how ADHD is reified. The analyses reveal four ways in which inattentive and restless behaviors are presented as an entity by means of the ADHD classification: language choice, logical fallacies, genetic reductionism, and textual silence. First, language choice, such as medical jargon and metaphors aid in representing ADHD as a disease entity. Second, several logical fallacies do the same, including the relatively unknown "ecological fallacy" that refers to the erroneous belief that average group findings, such as average brain size of groups of those with an ADHD classification, can be applied on an individual level. Third, genetic reductionism is often achieved by overstating the results of twin studies and being silent about the disappointing molecular genetic research. Such textual silence is the last identified mechanism of reification and includes instances in which societal factors that affect the ADHD construct are often omitted from texts, thereby obscuring the extent to which ADHD is a limited heuristic. It is essential that discourse communities do not repeat these four ways of reifying behavior and social relations into an alleged entity with the acronym ADHD. The errors and habits of writing may be epistemologically violent by influencing how laypeople and professionals see children and ultimately how children may come to see themselves in a negative way. Beyond that, if the institutional world shaped to help children is based on misguided assumptions, it may cause them harm and help perpetuate the misguided narrative. To counter the dominant, reifying and medicalizing view, guidelines such as the recently published "Dutch ADHD Psychoeducation Guidelines" might be helpful.

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

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%.

Comparison of Machine Learning predictive methods to diagnose the Attention Deficit/Hyperactivity Disorder levels using SPECT

ADHD (attention deficit hyperactivity disorder) is a neurodevelopmental disorder characterized by harmful levels of inattention, disorganization, and/or hyperactivity-impulsivity. In childhood, these symptoms often overlap with those of other disorders, and they tend to persist into adulthood, interfering with relationships and academic and work life. Diagnosis, traditionally made by assessing the patient, i.e., testing and listening to relatives and teachers, has already been aided by neuroimaging. However, the visual analysis of such images to make a psychiatric diagnosis is a complex and sometimes time-consuming task. For this reason, computer-aided diagnostic tools have increasingly evolved that, when combined with machine learning (ML) techniques, can accelerate, facilitate, and maximize the accuracy of diagnoses. Nevertheless, research evaluating ML models for classifying ADHD considering severity using images of the brain SPECT (Single Photon Emission Computed Tomography) is still very sparse. For this reason, this article aims to evaluate the performance of the ML methods: k-NN (k-Nearest Neighbors), Naive Bayes, Decision Tree, MLP (Multilayer Perceptron) and SVM (Support Vector Machine) in the classification of ADHD. The main goal of this analysis is to check whether the subjects have the disorder or not, and to classify the severity of those who have it using SPECT images. A database was created from SPECT images and diagnostic reports. After pre-processing these data, the best hyperparameters for the ML methods were searched, trained/tested and finally statistically compared. The best results were obtained with SVM and k-NN, with 98% accuracy. Although ADHD diagnosis by neuroimaging is not yet a standard clinical procedure, we argue that this study can contribute to ADHD diagnosis research and support methods for the development of CAD (computer-aided diagnosis) systems.