ArticleJournal of clinical medicine2019
Deep Learning Based on Event-Related EEG Differentiates Children with ADHD from Healthy Controls.
Article in Journal of clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- A Systematic Review on Feature Extraction in Electroencephalography-Based Diagnostics and Therapy in Attention Deficit Hyperactivity Disorder.Sensors (Basel, Switzerland) · 2022Pooled it
- A Multimodal Dataset for Neurophysiological and AI Applications.Scientific data · 2026Article
- New avenues for understanding what deep networks learn from EEG.Frontiers in robotics and AI · 2025Article
- Multi-modal EEG NEO-FFI with Trained Attention Layer (MENTAL) for mental disorder prediction.Brain informatics · 2024Article
- Investigating the role of auditory cues in modulating motor timing: insights from EEG and deep learning.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Vocal tasks-based EEG and speech signal analysis in children with neurodevelopmental disorders: a multimodal investigation.Cognitive neurodynamics · 2024Article
- ADHD/CD-NET: automated EEG-based characterization of ADHD and CD using explainable deep neural network technique.Cognitive neurodynamics · 2024Article
- Electroencephalogram-Based ConvMixer Architecture for Recognizing Attention Deficit Hyperactivity Disorder in Children.Brain sciences · 2024Article
- Deep-Learning-Based Automated Anomaly Detection of EEGs in Intensive Care Units.Bioengineering (Basel, Switzerland) · 2024Article
- Monitoring Changes in TMS-Evoked EEG and EMG Activity During 1 Hz rTMS of the Healthy Motor Cortex.eNeuro · 2024Article
- ADHD-AID: Aiding Tool for Detecting Children's Attention Deficit Hyperactivity Disorder via EEG-Based Multi-Resolution Analysis and Feature Selection.Biomimetics (Basel, Switzerland) · 2024Article
- Identifying ADHD and subtypes through microstates analysis and complex networks.Medical & biological engineering & computing · 2024Article
- Overactivated contextual visual perception and response to a single dose of methylphenidate in children with ADHD.European archives of psychiatry and clinical neuroscience · 2024Article
- Data leakage in deep learning studies of translational EEG.Frontiers in neuroscience · 2024Article
- LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain's Primes on Ulam's Spiral-Based Features with Electroencephalogram Signals.Sensors (Basel, Switzerland) · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms.Translational psychiatry · 2023Review
- Review
- Deep-Learning-Based ADHD Classification Using Children's Skeleton Data Acquired through the ADHD Screening Game.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Attention Deficit Hyperactivity Disorder (ADHD) is one of the most prevalent neuropsychiatric disorders in childhood and adolescence and its diagnosis is based on clinical interviews, symptom questionnaires, and neuropsychological testing. Much research effort has been undertaken to evaluate the usefulness of neurophysiological (EEG) data to aid this diagnostic process. In the current study, we applied deep learning methods on event-related EEG data to examine whether it is possible to distinguish ADHD patients from healthy controls using purely neurophysiological measures. The same was done to distinguish between ADHD subtypes. The results show that the applied deep learning model ("EEGNet") was able to distinguish between both ADHD subtypes and healthy controls with an accuracy of up to 83%. However, a significant fraction of individuals could not be classified correctly. It is shown that neurophysiological processes indicating attentional selection associated with superior parietal cortical areas were the most important for that. Using the applied deep learning method, it was not possible to distinguish ADHD subtypes from each other. This is the first study showing that deep learning methods applied to EEG data are able to dissociate between ADHD patients and healthy controls. The results show that the applied method reflects a promising means to support clinical diagnosis in ADHD. However, more work needs to be done to increase the reliability of the taken approach.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.