Attention deficit hyperactivity disorder detection in children using multivariate empirical EEG decomposition approaches: A comprehensive analytical study

Sharma, Yogesh; Singh, Bikesh Kumar

doi:10.1016/j.eswa.2022.119219

Cited by 13 publications

(10 citation statements)

References 65 publications

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“…The accuracy, sensitivity, specificity, F1-score, and MCC achieved by ADHD-AID are 0.991, 0.989, 0.992, 0.989, and 0.982, which are greater than those obtained by previous studies. The reason for this is that in contrast to other studies based on a larger number of features [ 21 , 24 ], ADHD-AID extracts thirty features including nonlinear features, band-power features, entropy-based features, and statistical features. Furthermore, ADHD-AID acquires these features from time and time–frequency domains of several multi-resolution analysis approaches such as VMD, DWT, and EWT, which is not the case in previous studies [ 21 , 23 , 24 , 26 , 36 ].…”

Section: Discussionmentioning

confidence: 99%

“…The reason for this is that in contrast to other studies based on a larger number of features [ 21 , 24 ], ADHD-AID extracts thirty features including nonlinear features, band-power features, entropy-based features, and statistical features. Furthermore, ADHD-AID acquires these features from time and time–frequency domains of several multi-resolution analysis approaches such as VMD, DWT, and EWT, which is not the case in previous studies [ 21 , 23 , 24 , 26 , 36 ]. In addition, it employs an FS approach to select the influential features that impact performance, in contrast to other studies [ 23 , 31 , 36 , 62 ].…”

Section: Discussionmentioning

confidence: 99%

“…However, feature extraction is the main element for the successful identification and classification of brain waves. Several studies have employed multiple feature extraction and machine learning approaches; among them, the research article [ 21 ] employed three empirical decomposition approaches including EWT, empirical mode decomposition (EMD), and empirical variational decomposition (EVD). The authors extracted 15 features from each domain of the EWT, EMD, and EVD independently.…”

Section: Related Workmentioning

confidence: 99%

“…Several studies have been conducted regarding developing aiding tools based on artificial intelligence technology to speed up and improve the precision of the entire procedure of detecting neurological and neurodevelopment diseases [ 15 , 16 , 17 , 18 ]. The latest research has focused on initial ADHD recognition via several brain imaging modalities such as electroencephalography (EEG) [ 19 ], magnetoencephalography [ 20 ], magnetic resonance imaging [ 21 ], functional magnetic resonance imaging [ 22 ], and others. Among all these approaches, the EEG method has emerged as one of the most popular methods for diagnosing ADHD [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

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ADHD-AID: Aiding Tool for Detecting Children’s Attention Deficit Hyperactivity Disorder via EEG-Based Multi-Resolution Analysis and Feature Selection

Attallah

2024

Biomimetics

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The severe effects of attention deficit hyperactivity disorder (ADHD) among adolescents can be prevented by timely identification and prompt therapeutic intervention. Traditional diagnostic techniques are complicated and time-consuming because they are subjective-based assessments. Machine learning (ML) techniques can automate this process and prevent the limitations of manual evaluation. However, most of the ML-based models extract few features from a single domain. Furthermore, most ML-based studies have not examined the most effective electrode placement on the skull, which affects the identification process, while others have not employed feature selection approaches to reduce the feature space dimension and consequently the complexity of the training models. This study presents an ML-based tool for automatically identifying ADHD entitled “ADHD-AID”. The present study uses several multi-resolution analysis techniques including variational mode decomposition, discrete wavelet transform, and empirical wavelet decomposition. ADHD-AID extracts thirty features from the time and time–frequency domains to identify ADHD, including nonlinear features, band-power features, entropy-based features, and statistical features. The present study also looks at the best EEG electrode placement for detecting ADHD. Additionally, it looks into the location combinations that have the most significant impact on identification accuracy. Additionally, it uses a variety of feature selection methods to choose those features that have the greatest influence on the diagnosis of ADHD, reducing the classification’s complexity and training time. The results show that ADHD-AID has provided scores for accuracy, sensitivity, specificity, F1-score, and Mathew correlation coefficients of 0.991, 0.989, 0.992, 0.989, and 0.982, respectively, in identifying ADHD with 10-fold cross-validation. Also, the area under the curve has reached 0.9958. ADHD-AID’s results are significantly higher than those of all earlier studies for the detection of ADHD in adolescents. These notable and trustworthy findings support the use of such an automated tool as a means of assistance for doctors in the prompt identification of ADHD in youngsters.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ADHD-AID: Aiding Tool for Detecting Children’s Attention Deficit Hyperactivity Disorder via EEG-Based Multi-Resolution Analysis and Feature Selection

Attallah

2024

Biomimetics

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“…However, these methods often face the challenge of effectively capturing the spatial and channel dependence of EEG signals, which is crucial for accurate diagnosis [18]. Some studies have explored the use of EEG analysis in children with attention-deficit/hyperactivity disorder (ADHD), utilizing various techniques such as spatial normalization, smoothing, and multivariate empirical decomposition [19][20][21]. These studies highlight the lack of biomarkers for ADHD diagnosis and the complexity and challenges associated with capturing the spatial and channel dependence of EEG signals in this disorder.…”

Section: Introductionmentioning

confidence: 99%

Electroencephalogram-Based ConvMixer Architecture for Recognizing Attention Deficit Hyperactivity Disorder in Children

Feng,

2024

Brain Sciences

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Attention deficit hyperactivity disorder (ADHD) is a neuro-developmental disorder that affects approximately 5–10% of school-aged children worldwide. Early diagnosis and intervention are essential to improve the quality of life of patients and their families. In this study, we propose ConvMixer-ECA, a novel deep learning architecture that combines ConvMixer with efficient channel attention (ECA) blocks for the accurate diagnosis of ADHD using electroencephalogram (EEG) signals. The model was trained and evaluated using EEG recordings from 60 healthy children and 61 children with ADHD. A series of experiments were conducted to evaluate the performance of the ConvMixer-ECA. The results showed that the ConvMixer-ECA performed well in ADHD recognition with 94.52% accuracy. The incorporation of attentional mechanisms, in particular ECA, improved the performance of ConvMixer; it outperformed other attention-based variants. In addition, ConvMixer-ECA outperformed state-of-the-art deep learning models including EEGNet, CNN, RNN, LSTM, and GRU. t-SNE visualization of the output of this model layer validated the effectiveness of ConvMixer-ECA in capturing the underlying patterns and features that separate ADHD from typically developing individuals through hierarchical feature learning. These outcomes demonstrate the potential of ConvMixer-ECA as a valuable tool to assist clinicians in the early diagnosis and intervention of ADHD in children.

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Feature engineering of EEG applied to mental disorders: a systematic mapping study

García-Ponsoda,

García-Carrasco,

Teruel

et al. 2023

Appl Intell

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Around a third of the total population of Europe suffers from mental disorders. The use of electroencephalography (EEG) together with Machine Learning (ML) algorithms to diagnose mental disorders has recently been shown to be a prominent research area, as exposed by several reviews focused on the field. Nevertheless, previous to the application of ML algorithms, EEG data should be correctly preprocessed and prepared via Feature Engineering (FE). In fact, the choice of FE techniques can make the difference between an unusable ML model and a simple, effective model. In other words, it can be said that FE is crucial, especially when using complex, non-stationary data such as EEG. To this aim, in this paper we present a Systematic Mapping Study (SMS) focused on FE from EEG data used to identify mental disorders. Our SMS covers more than 900 papers, making it one of the most comprehensive to date, to the best of our knowledge. We gathered the mental disorder addressed, all the FE techniques used, and the Artificial Intelligence (AI) algorithm applied for classification from each paper. Our main contributions are: (i) we offer a starting point for new researchers on these topics, (ii) we extract the most used FE techniques to classify mental disorders, (iii) we show several graphical distributions of all used techniques, and (iv) we provide critical conclusions for detecting mental disorders. To provide a better overview of existing techniques, the FE process is divided into three parts: (i) signal transformation, (ii) feature extraction, and (iii) feature selection. Moreover, we classify and analyze the distribution of existing papers according to the mental disorder they treat, the FE processes used, and the ML techniques applied. As a result, we provide a valuable reference for the scientific community to identify which techniques have been proven and tested and where the gaps are located in the current state of the art. Graphical Abstract

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Attention deficit hyperactivity disorder detection in children using multivariate empirical EEG decomposition approaches: A comprehensive analytical study

Cited by 13 publications

References 65 publications

ADHD-AID: Aiding Tool for Detecting Children’s Attention Deficit Hyperactivity Disorder via EEG-Based Multi-Resolution Analysis and Feature Selection

ADHD-AID: Aiding Tool for Detecting Children’s Attention Deficit Hyperactivity Disorder via EEG-Based Multi-Resolution Analysis and Feature Selection

Electroencephalogram-Based ConvMixer Architecture for Recognizing Attention Deficit Hyperactivity Disorder in Children

Feature engineering of EEG applied to mental disorders: a systematic mapping study

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