ArticleIET systems biology2019
Diagnosis of attention deficit hyperactivity disorder using non-linear analysis of the EEG signal.
Article in IET systems biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Entropy difference-based EEG channel selection technique for automated detection of ADHD.PloS one · 2025Article
- Article
- Impact of visual distractors in virtual reality environments on sustained attention behavioral performance and EEG characteristics.Frontiers in human neuroscience · 2025Article
- Convolutional neural network framework for EEG-based ADHD diagnosis in children.Health information science and systems · 2024Article
- Acoustic and Text Features Analysis for Adult ADHD Screening: A Data-Driven Approach Utilizing DIVA Interview.IEEE journal of translational engineering in health and medicine · 2024Article
- Identification of attention deficit hyperactivity disorder with deep learning model.Physical and engineering sciences in medicine · 2023Article
- Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms.Translational psychiatry · 2023Review
- Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations.The European journal of neuroscience · 2022Review
- Electroencephalography complexity in resting and task states in adults with attention-deficit/hyperactivity disorder.Brain communications · 2022Article
- EEG based cognitive task classification using multifractal detrended fluctuation analysis.Cognitive neurodynamics · 2021Article
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Authors and funding
3 authors.
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Abstract
Attention deficit hyperactivity disorder (ADHD) is a common behavioural disorder that may be found in 5%-8% of the children. Early diagnosis of ADHD is crucial for treating the disease and reducing its harmful effects on education, employment, relationships, and life quality. On the other hand, non-linear analysis methods are widely applied in processing the electroencephalogram (EEG) signals. It has been proved that the brain neuronal activity and its related EEG signals have chaotic behaviour. Hence, chaotic indices can be employed to classify the EEG signals. In this study, a new approach is proposed based on the combination of some non-linear features to distinguish ADHD from normal children. Lyapunov exponent, fractal dimension, correlation dimension and sample, fuzzy and approximate entropies are the non-linear extracted features. For computing, the chaotic time series of obtained EEG in the brain frontal lobe (FP1, FP2, F3, F4, and Fz) need to be analysed. Experiments on a set of EEG signal obtained from 50 ADHD and 26 normal cases yielded a sensitivity, specificity, and accuracy of 98, 92.31, and 96.05%, respectively. The obtained accuracy provides a significant improvement in comparison to the other similar studies in identifying and classifying children with ADHD.
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