Identification of ADHD Cognitive Pattern Disturbances Using EEG and Wavelets Analysis

Gabriel, Rogrio; Spendola, Marilda Machado; Mesquita, Alexandre; Neto, Angelo Zerbetto

doi:10.1109/bibe.2017.00-62

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…In literature, many researchers have utilized different methods to analyze resting-state EEG (rsEEG) and time series recorded from an ADHD group and a control group. Authors [44,45] contributed to discovering the best indicator of attention for ADHD. They applied the wavelet decomposition method to obtain different frequency bands of EEG.…”

Section: Classification Of Eeg Signals For Adhdmentioning

confidence: 99%

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A Review of EEG Signal Analysis for Diagnosis of Neurological Disorders using Machine Learning

Joshi¹,

Nanavati²

2022

Journal of Biomedical Photonics & Engineering

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Neurological disorders are diseases that affect the brain and the central autonomic nervous systems. These disorders take a huge toll on an individual's health and general well-being. After cardiovascular diseases, neurological disorders are the main cause of death. These disorders include epilepsy, Alzheimer’s disease, dementia, cerebrovascular diseases including stroke, migraine, Parkinson’s disease and numerous other disorders. This manuscript presents a state-of-the-art consolidated review of research on the diagnosis of the three most common neurological disorders using electroencephalogram (EEG) signals with machine learning techniques. The disorders discussed in this manuscript are the more prevalent disorders like epilepsy, Attention-deficit/hyperactivity disorder (ADHD), and Alzheimer’s disease. This manuscript helps in understanding the details about EEG signal processing for diagnosis and analysis of neurological disorders along with a discussion of the datasets, limitations, results and research scope of the various techniques.

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Section: Classification Of Eeg Signals For Adhdmentioning

confidence: 99%

“…Similarity patients are asked to do some motor activity like to arrange the spoons and hand them over to their caretaker. Moreover, visual cues activity as colors, numbers, and animals, are identified to find out the attentive and cognitive capability [44].…”

Section: Assessment Of Cognitive Capabilitymentioning

confidence: 99%

A Review of EEG Signal Analysis for Diagnosis of Neurological Disorders using Machine Learning

Joshi¹,

Nanavati²

2022

Journal of Biomedical Photonics & Engineering

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“…The study [31] compared cobalt level in urine sample, result proved that cobalt is not at all responsible for the presence of ADHD disorder. The study [32] uses EEG and wavelet analysis to construct a model which is capable to separate ADHD and non ADHD with 94.74% accuracy. The work [33] uses quantitative data to list students profiles, the profiles leads to identify where students finding difficult to linguistic, cognitive and environmental natures.…”

Section: Classification Of Applications Using Feature Selectionmentioning

confidence: 99%

Classification of Attention Deficit Hyperactivity Disorder (ADHD) Considering Diagnosis and Treatment

Sumathi¹,

Cholli²,

Nayak³

2019

IJMECS

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Attention Deficit Hyperactivity Disorder (ADHD) is the most frequent brain disorders in children. Brain is the greatest complicated data processing part in human body. ADHD can begin in childhood age and may extend till adolescent too. ADHD patients activities/actions/behaviour are totally different from non ADHD patients. To solve the problem in early stage is more precious contribution for children life. Otherwise the disorder may cause further destruction in child brain. An activity of ADHD child is: carelessness, impulsive, and feverish. These activities may be common in other children too but for ADHD patients these activities are more severe and more often occurs. ADHD can arise problems at school, home, it may affect children learning ability, and child may not join with others. ADHD is one among many childhood syndromes. The paper summarises the different ADHD diagnosis methods and suggested treatments for the disorder.

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Interpretable Diagnosis of ADHD Based on Wavelet Features and Logistic Regression

Pastrana-Cortes

Maya-Piedrahita

Herrera-Gómez

et al. 2021

Progress in Artificial Intelligence and Pattern Recognition

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Identification of ADHD Cognitive Pattern Disturbances Using EEG and Wavelets Analysis

Cited by 9 publications

References 20 publications

A Review of EEG Signal Analysis for Diagnosis of Neurological Disorders using Machine Learning

A Review of EEG Signal Analysis for Diagnosis of Neurological Disorders using Machine Learning

Classification of Attention Deficit Hyperactivity Disorder (ADHD) Considering Diagnosis and Treatment

Interpretable Diagnosis of ADHD Based on Wavelet Features and Logistic Regression

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