Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning

Ahire, Nitin; Awale, R.N.; Wagh, Abhay

doi:10.1080/23279095.2023.2247702

Cited by 9 publications

(4 citation statements)

References 34 publications

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“…The researchers have used several techniques for feature extraction in the analysis of EEG data. These techniques include statistical features and deep-learning-based features, which have been extensively utilized [49][50][51][52]. The ADHD may also be diagnosed using EEG data, hence necessitating the extraction of characteristics from these signals [53,54].…”

Section: Background Of the Studymentioning

confidence: 99%

Developing System-Based Artificial Intelligence Models for Detecting the Attention Deficit Hyperactivity Disorder

Alkahtani,

Aldhyani,

Ahmed

et al. 2023

Mathematics

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This study presents a novel methodology for automating the classification of pediatric ADHD using electroencephalogram (EEG) biomarkers through machine learning and deep learning techniques. The primary objective is to develop accurate EEG-based screening tools to aid clinical diagnosis and enable early intervention for ADHD. The proposed system utilizes a publicly available dataset consisting of raw EEG recordings from 61 individuals with ADHD and 60 control subjects during a visual attention task. The methodology involves meticulous preprocessing of raw EEG recordings to isolate brain signals and extract informative features, including time, frequency, and entropy signal characteristics. The feature selection techniques, including least absolute shrinkage and selection operator (LASSO) regularization and recursive elimination, were applied to identify relevant variables and enhance generalization. The obtained features are processed by employing various machine learning and deep learning algorithms, namely CatBoost, Random Forest Decision Trees, Convolutional Neural Networks (CNNs), and Long Short-Term Memory Networks (LSTMs). The empirical results of the proposed algorithms highlight the effectiveness of feature selection approaches in matching informative biomarkers with optimal model classes. The convolutional neural network model achieves superior testing accuracy of 97.75% using LASSO-regularized biomarkers, underscoring the strengths of deep learning and customized feature optimization. The proposed framework advances EEG analysis to uncover discriminative patterns, significantly contributing to the field of ADHD screening and diagnosis. The suggested methodology achieved high performance compared with different existing systems based on AI approaches for diagnosing ADHD.

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Section: Background Of the Studymentioning

confidence: 99%

Developing System-Based Artificial Intelligence Models for Detecting the Attention Deficit Hyperactivity Disorder

Alkahtani,

Aldhyani,

Ahmed

et al. 2023

Mathematics

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“…Their dataset combines real and simulated stock trading [9] emotions, including fear, sadness, hope, and calmness. We use machine learning algorithms [10] to construct an emotion recognition system to solve the problem of stock emotion dataset classification [11].…”

Section: Introductionmentioning

confidence: 99%

Application of EEG-based Machine Learning in Stock Trading-related Emotion Recognition

Zuo,

Yu,

Sui

2024

Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27–29,

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This paper develops a stock emotion recognition system based on a valence/arousal model using electroencephalogram (EEG) signals. The dataset is collected from participants who engage in paper trading using real stock market data, virtual currencies, and emotional outputs. The dataset contains five frequency bands, features such as differential entropy (DE), differential asymmetry (DASM), and rational asymmetry (RASM). Feature selection is performed using mutual information-based filtering combined with chi-square statistics and embedded algorithms in deep learning classifiers. Stock sentiment classification uses established machine learning models, stochastic gradient descent (SGD), linear discriminant analysis (LDA), K nearest neighbor (KNN) and naive Bayes (NB) algorithms. Subsequently, a comprehensive performance analysis and comparative evaluation of each classification algorithm are conducted. Notably, the K-nearest neighbor (KNN) method achieves remarkable accuracy rates of 89.2% for arousal and 94.59% for valence. These results highlight its exceptional performance when compared to pre-existing algorithms applied to stock emotion datasets.

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“…Autism Spectrum enhance accuracy, and also facilitate comprehension of the techniques and algorithms employed for various types of data. Multiple studies have been conducted on ASD [22][23][24][25][26][27][28], ADHD [29][30][31], ID [9,10,32], SLD [33,34], CD [35], and NDs [4,19,20,31,[36][37][38], providing evidence that ML algorithms can enhance diagnostic strategies for NDs. Further, more research efforts that seek to investigate ML approaches for early detection and diagnosis of NDs in real-life situations are crucial for ensuring timely intervention and optimizing lifelong outcomes [17,19,20,[39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution

Toki,

Tatsis,

Pange

et al. 2023

Applied Sciences

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Developmental domains refer to different areas of a child’s growth and maturation, including physical, language, cognitive, and social–emotional skills. Understanding these domains helps parents, caregivers, and professionals track a child’s progress and identify potential areas of concern. Nevertheless, due to the high level of heterogeneity and overlap, neurodevelopmental disorders may go undiagnosed in children for a crucial period. Detecting neurodevelopmental disorders at an early stage is fundamental. Digital tools like artificial intelligence can help clinicians with the early detection process. To achieve this, a new method has been proposed that creates artificial features from the original ones derived from the SmartSpeech project, using a feature construction procedure guided by the Grammatical Evolution technique. The new features from a machine learning model are used to predict neurodevelopmental disorders. Comparative experiments demonstrated that using the feature creation method outperformed other machine learning methods for predicting neurodevelopmental disorders. In many cases, the reduction in the test error reaches up to 65% to the next better one.

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Electroencephalogram (EEG) based prediction of attention deficit hyperactivity disorder (ADHD) using machine learning

Cited by 9 publications

References 34 publications

Developing System-Based Artificial Intelligence Models for Detecting the Attention Deficit Hyperactivity Disorder

Developing System-Based Artificial Intelligence Models for Detecting the Attention Deficit Hyperactivity Disorder

Application of EEG-based Machine Learning in Stock Trading-related Emotion Recognition

Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution

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