Attention Deficit Hyperactivity Disorder Diagnosis using non-linear univariate and multivariate EEG measurements: a preliminary study

Rezaeezadeh, Maryam; Shamekhi, Sina; Shamsi, Mousa

doi:10.1007/s13246-020-00858-3

Cited by 45 publications

(24 citation statements)

References 40 publications

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“…Bob et al (2011) using pointwise correlation dimension to analyze attentional processes related to dissociative states. Rezaeezadeh et al (2020) focused on entropy measures, including univariate features from individual EEG channels and multivariate features from brain lobes, to diagnosis Attention Deficit Hyperactivity Disorder. Recent studies have shown that applying nonlinear multiscale information analysis to EEG can provide new information about the complex dynamics of brain cognitive function, such as emotion recognition (Gao et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Frontal EEG-Based Multi-Level Attention States Recognition Using Dynamical Complexity and Extreme Gradient Boosting

Wan

Cui

Gao

et al. 2021

Front. Hum. Neurosci.

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Measuring and identifying the specific level of sustained attention during continuous tasks is essential in many applications, especially for avoiding the terrible consequences caused by reduced attention of people with special tasks. To this end, we recorded EEG signals from 42 subjects during the performance of a sustained attention task and obtained resting state and three levels of attentional states using the calibrated response time. EEG-based dynamical complexity features and Extreme Gradient Boosting (XGBoost) classifier were proposed as the classification model, Complexity-XGBoost, to distinguish multi-level attention states with improved accuracy. The maximum average accuracy of Complexity-XGBoost were 81.39 ± 1.47% for four attention levels, 80.42 ± 0.84% for three attention levels, and 95.36 ± 2.31% for two attention levels in 5-fold cross-validation. The proposed method is compared with other models of traditional EEG features and different classification algorithms, the results confirmed the effectiveness of the proposed method. We also found that the frontal EEG dynamical complexity measures were related to the changing process of response during sustained attention task. The proposed dynamical complexity approach could be helpful to recognize attention status during important tasks to improve safety and efficiency, and be useful for further brain-computer interaction research in clinical research or daily practice, such as the cognitive assessment or neural feedback treatment of individuals with attention deficit hyperactivity disorders, Alzheimer’s disease, and other diseases which affect the sustained attention function.

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Section: Introductionmentioning

confidence: 99%

Frontal EEG-Based Multi-Level Attention States Recognition Using Dynamical Complexity and Extreme Gradient Boosting

Wan

Cui

Gao

et al. 2021

Front. Hum. Neurosci.

View full text Add to dashboard Cite

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“…Non-linear analysis of EEG waves has revealed new information on the complex dynamics of underlying neural networks [47]. Well-known entropy methods like Sample Entropy (SampEn), Dispersion entropy (DispEn), Multivariate Sample Entropy (mvSE), Approximate entropy, sample entropy are used to classify EEG signals [48]. Likewise, in study [49], fractal dimension as Higuchi's, Katz's, and Petrosian's has been applied to discriminate ADHD and healthy controls.…”

Section: Classification Of Eeg Signals For Adhdmentioning

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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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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“…The proposed model not only uses PSD parameters and their derivatives (relative and absolute), but also makes use of nonlinear metrics such as entropy derivatives, such as Shannon's entropy and Logarithmic entropy. In this respect, it has been found that the use of these nonlinear metrics provides differential values when assessing different EEG patterns [72,73].…”

Section: Multi-parametric Classifiers Modelmentioning

confidence: 99%

Physical Exercise Effects on University Students’ Attention: An EEG Analysis Approach

et al. 2022

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Physically active breaks (AB) are currently being proposed as an interesting tool to improve students’ attention. Reviews and meta-analyses confirm their effect on attention, but also warned about the sparse evidence based on vigilance and university students. Therefore, this pilot study aimed to (a) determine the effects of AB in comparison with passive breaks on university students’ vigilance and (b) to validate an analysis model based on machine learning algorithms in conjunction with a multiparametric model based on electroencephalography (EEG) signal features. Through a counterbalanced within-subject experimental study, six university students (two female; mean age = 25.67, STD = 3.61) had their vigilance performances (i.e., response time in Psycho-Motor Vigilance Task) and EEG measured, before and after a lecture with an AB and another lecture with a passive break. A multiparametric model based on the spectral power, signal entropy and response time has been developed. Furthermore, this model, together with different machine learning algorithms, shows that for the taken signals there are significant differences after the AB lesson, implying an improvement in attention. These differences are most noticeable with the SVM with RBF kernel and ANNs with F1-score of 85% and 88%, respectively. In conclusion, results showed that students performed better on vigilance after the lecture with AB. Although limited, the evidence found could help researchers to be more accurate in their EEG analyses and lecturers and teachers to improve their students’ attentions in a proper way.

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Attention Deficit Hyperactivity Disorder Diagnosis using non-linear univariate and multivariate EEG measurements: a preliminary study

Cited by 45 publications

References 40 publications

Frontal EEG-Based Multi-Level Attention States Recognition Using Dynamical Complexity and Extreme Gradient Boosting

Frontal EEG-Based Multi-Level Attention States Recognition Using Dynamical Complexity and Extreme Gradient Boosting

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

Physical Exercise Effects on University Students’ Attention: An EEG Analysis Approach

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