Early Detection Of Attention Deficit Hyperactivity Disorder Research Articles

Application of ANFIS-based Non-Linear Regression Modelling to Predict Concentration Level in Concentration Grid Test as Early Detection of ADHD in Children

Concentration is the main asset for students and serves as an indicator of successful learning implementation. One of the abnormal disturbances that can occur in a child's concentration development is attention deficit hyperactivity disorder (ADHD). The prevalence of ADHD in Indonesia in 2014 reached 12.81 million people due to delayed management in addressing ADHD. Therefore, early detection of ADHD is necessary for prevention. ADHD detection can be done by testing the level of concentration using a concentration grid. However, a method is needed that can be applied to uncooperative young children who are not familiar with numbers. Therefore, research was conducted with an innovative approach using a combination of EEG-ECG to classify concentration levels. The data used in this study were primary data from 4 participants with 5 repetitions. The data were processed in the preprocessing stage, which involved noise filtering and Butterworth filtering. The features used in this study were BPM (beats per minute), alpha, theta, and beta EEG signals, which would later become inputs for the Adaptive Neuro-Fuzzy Inference System (ANFIS). The output shows that the combination of EEG-ECG has the potential to predict concentration test results. Using BPM, alpha, theta, and beta signals can serve as parameters for predicting the concentration grid test values using ANFIS effectively. In the ANFIS model with 4 features, an accuracy of 99.997% was obtained for the training data and 80.2142% for the testing data. This result could be developed for early detection of ADHD based on concentration levels so the learning implementation could be more effective.

Automatic Identification of Children with ADHD from EEG Brain Waves

EEG (electroencephalogram) signals could be used reliably to extract critical information regarding ADHD (attention deficit hyperactivity disorder), a childhood neurodevelopmental disorder. The early detection of ADHD is important to lessen the development of this disorder and reduce its long-term impact. This study aimed to develop a computer algorithm to identify children with ADHD automatically from the characteristic brain waves. An EEG machine learning pipeline is presented here, including signal preprocessing and data preparation steps, with thorough explanations and rationale. A large public dataset of 120 children was selected, containing large variability and minimal measurement bias in data collection and reproducible child-friendly visual attentional tasks. Unlike other studies, EEG linear features were extracted to train a Gaussian SVM-based model from only the first four sub-bands of EEG. This eliminates signals more than 30 Hz, thus reducing the computational load for model training while keeping mean accuracy of ~94%. We also performed rigorous validation (obtained 93.2% and 94.2% accuracy, respectively, for holdout and 10-fold cross-validation) to ensure that the developed model is minimally impacted by bias and overfitting that commonly appear in the ML pipeline. These performance metrics indicate the ability to automatically identify children with ADHD from a local clinical setting and provide a baseline for further clinical evaluation and timely therapeutic attempts.

Classification of the Children with ADHD and Healthy Children Based on the Directed Phase Transfer Entropy of EEG Signals

Purpose: The present study was conducted to investigate and classify two groups of healthy children and children with Attention Deficit Hyperactivity Disorder (ADHD) by Effective Connectivity (EC) measure. Since early detection of ADHD can make the treatment process more effective, it is important to diagnose it using new methods. 
 Materials and Methods: For this purpose, Effective Connectivity Matrices (ECMs) were constructed based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children of the same age. ECMs of each individual were obtained by the directed Phase Transfer Entropy (dPTE) between each pair of electrodes. ECMs were calculated in five frequency bands including, delta, theta, alpha, beta, and gamma. Based on ECM, an Effective Connectivity Vector (ECV) was constructed as a feature vector for the classification process. Furthermore, ECV of different frequency bands was pooled in one global ECV (gECV). Multilayer Artificial Neural Network (ANN) was used in the steps of classification and feature selection by the Genetic Algorithm (GA).
 Results: The highest classification accuracy with the selected features of ECV was related to theta frequency band with 89.7%. After that, the delta frequency band had the highest accuracy with 89.2%. The results of ANN classification and GA on the gECV reported 89.1% of accuracy.
 Conclusion: Our findings show that the dPTE measure, which determines effective connectivity between the brain regions, can be used to classify between ADHD and healthy groups. The results of the classification have improved compared to some studies that used the functional connectivity measures.

Temporal perception and delay aversion: A videogame screening tool for the early detection of ADHD

Attention-deficit/hyperactivity disorder (ADHD) is a behavioral syndrome where inattention and/or hyperactivity impair social, academic or occupational function. Cognitive impairments in temporal abilities and delay aversion have been related to ADHD. The aim of this study is to design a game-like software that assesses temporal perception for the early detection of ADHD. Method: Two groups of 7 to 10 children (17 with ADHD and 17 control children) were compared on their performance on four game-like software based on Delay Aversion, Anticipation, Synchronization Tapping and Time reproduction experimental tasks. Results: All games detected significant differences between groups in accuracy or variability. The Discriminant function correctly classified 82.4% of the cases. Conclusions: Although the conversion of experimental tasks into game-like software implies a compromise between experimental rigour and screening efficiency, the use of videogames to measure cognitive processes is a potential tool for preventive healthcare.

Traffic and Criminal Behavior of Adults with Attention Deficit–Hyperactivity with a Prospective Follow-Up from Birth to the Age of 40 Years

Objective: The purpose of this study was to investigate whether individuals with attention deficit–hyperactivity disorder (ADHD) followed up to age 40 have a higher mortality, more involvement in criminal behavior, increased traffic accidents, and frequency or registered violations against traffic rules or whether they have been more frequently victims to crimes.Methods: The ADHD cohort (N = 122) born in 1971–1974 was isolated at the age of 9 years from the base cohort of 865 children who had known risk factors at birth and were still alive at the age of 5 years. Ninety-four healthy individuals born during the same years served as control subjects. None of the individuals with ADHD had used psychostimulants before their adolescence. The follow-up data were available from the newborn period until the ages of 5 and 9 years. At the ages of 16 and 30, the data were collected via questionnaire. For this study, the national police registers (last 5 years) were examined for traffic violations, crimes, or being an object of a criminal act when the persons reached the age of 40 years.Results: Ten men and one woman with ADHD but none of the controls had died by the age of 40. Three died of disease-related incidents, and 8 (13%) died of abnormal causes such as suicide (3), traffic accident (2), substance abuse (2), or violence (1). During the follow-up period, individuals with ADHD had been involved in violent behavior or economic criminality more frequently than the control subjects. They were also more commonly victims of criminal acts. No difference was found in traffic citations between those with ADHD and control subjects (at 35–40 years) when all traffic crimes were considered. A difference was not observed in the frequency of traffic accidents. However, there was a significant difference in drunk driving (at the ages of 30 and 35–40) and the number of persons without a driver's license.Discussion: Subjects with ADHD showed an elevated risk of being involved in criminality and had a higher risk of dying before the age of 40 years. The early detection of ADHD in childhood and appropriate treatments and family support may decrease criminality and save both money and human distress.

Attention Deficit/Hyperactivity Disorder (ADHD) in a Group of Patients with Addictive Problems: Exploratory Study in France

High rates of childhood ADHD has been found among substance users, revealing an important role of ADHD in addictive disorders. Compared to non-hyperactive, people with ADHD reported higher rates of co-accuracy with substance use. ADHD appears to be a risk factor for developing a consummation of one of these substances and complicates even access to the abstinence. The present study examines the prevalence of ADHD in adult patients with addictive problems (alcoholism, tobacco consumption, pathological gambling and compulsive buying). The authors compared 117 patients with and without ADHD, according to evaluations made with the Wender Utah Rating Scale (WURS) and the Adult Deficit Disorders Scale [1]. Various differences were observed between the group with ADHD and that without ADHD. The percentage of adults with positive scores in scales of ADHD in the sample group was 35%, compared to average 3.4% [2] in the general population. Different trends were observed, in accord to literature, the age at beginning of addictive behaviours are more earlier in ADHD group, and the cigarette consummation are as well elevated in ADHD group. Beside the use of psychoactive substances (cocaine, heroin and cannabis) are more elevated, as well as cognitive and motor impulsivity (Barratt Impulsivity Scale BIS-10) in the ADHD group. These data show the value of early detection of ADHD in order to prevent complications during adolescence and adulthood.