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

Sumathi, M.; Cholli, Nagaraj G; Nayak, Shantharam

doi:10.5815/ijmecs.2019.06.04

Cited by 3 publications

(2 citation statements)

References 68 publications

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“…Different diagnosis and analysis methods for ADHD are found in the literature. A more comprehensive description can be found in study [43]. Some specific symptoms of the disorder are simpler to identify than other disorder's symptoms.…”

Section: Diagnosis Of Adhdmentioning

confidence: 99%

“…In order to investigate human cognitive patterns, Event-related potentials (ERPs) visual stimulation with the Oddball paradigm is used as the basis. For instance, the probable region of the brain that shows abnormality due to ADHD syndrome can be used for assessment of learning capability of ADHD-affected children [43]. Similarity patients are asked to do some motor activity like to arrange the spoons and hand them over to their caretaker.…”

Section: Assessment Of Cognitive Capabilitymentioning

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: Diagnosis Of Adhdmentioning

confidence: 99%

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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A Robust Movement Quantification Algorithm of Hyperactivity Detection for ADHD Children Based on 3D Depth Images

et al. 2022

IEEE Trans. on Image Process.

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Normalization and deep learning based attention deficit hyperactivity disorder classification

Preetha

Mallika

2021

IFS

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Attention Deficit Hyperactivity Disorder (ADHD) is one of the major mental-health disorders worldwide. ADHD is typically characterized by impaired executive function, impulsivity, hyperactivity and with respect to these behavioral symptoms, diagnosis of ADHD is performed. These symptoms are obviously seen at in early stage. Serious impairments and substantial burdens are induced for society as well as to families. However, for ADHD, there is no diagnostic laboratory in current scenario. Psychological tests like Brown Attention Deficit Disorder Scale (BADDS), Conners Parent Rating Scale and ADHD Rating Scale (ADHD-RS) are carried out for ADHD diagnosis. Tedious and complex clinical analysis are needed in this testing and this makes low efficiency of the diagnostic process. A traditional diagnosis technique of ADHD produces degraded results. So, enhanced extreme learning machine is incorporated with existing techniques for avoiding the issues of performance degradation. There is a need to enhance the classifier performance further and there is a chance for unwanted noise in input samples, which may degrade the performance of classifier. For avoiding these issues, an enhanced and automated ADHS diagnosis technique is proposed. First stage is pre-processing, and it is carried out based on min max normalization and feature extraction is a next stage, which is carried out through Fast Independent Component Analysis and third stage is a Deep Extreme Learning Machine (DELM) based ADHD identification and classification. Extreme Learning Machine with Kernel (KELM) and Multilayer Extreme Learning Machine (MLELM) algorithm are combined in this method and it is termed as deep extreme learning machine (DELM). Collection of neuro images are used for quantitative and qualitative analysis and with respect to f-measure, recall, precision and accuracy, robustness of proposed technique is demonstrated.

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

Cited by 3 publications

References 68 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

A Robust Movement Quantification Algorithm of Hyperactivity Detection for ADHD Children Based on 3D Depth Images

Normalization and deep learning based attention deficit hyperactivity disorder classification

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