Phase Space Reconstruction of EEG Signals for Classification of ADHD and Control Adults

Kaur, Simranjit; Singh, Sukhwinder; Arun, Priti; Kaur, Damanjeet; Bajaj, Manoj Kumar

doi:10.1177/1550059419876525

Cited by 37 publications

(21 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…15,16 In 2019, Kaur et al used SVM method to predict ADHD in accordance with electroencephalography data, and the prediction accuracy reached 93.3%, which was higher than the accuracy obtained in this study. 17 This finding suggests that in the future, the diagnosis of ADHD should be a comprehensive reference for a variety of assessment results.…”

Section: Discussionmentioning

confidence: 99%

<p>A Predictive Model Offor Attention Deficit Hyperactivity Disorder Based on Clinical Assessment Tools</p>

Han¹,

Fang²,

Hong³

2020

NDT

View full text Add to dashboard Cite

Background: At present, clinicians diagnose that the clinical diagnosis of attention deficit hyperactivity disorder (ADHD) in children is mainly on the basis of the information provided by their parents, the behaviour of children in clinical clinics and the assessments of clinical rating scales and neuropsychological tests. Notably, no unified standard exists currently for analysing the results of various measurement tools for diagnosing ADHD. Therefore, clinicians interpret the results of clinical rating scales and neuropsychological tests entirely based on their clinical experience. Methods and subjects:: To provide guidance for clinicians on how to analyse the results of various clinical assessment tools when diagnosing ADHD, this study assessed children with ADHD and children in the control group using two clinical assessment scales-parent rating scale (PSQ) and Child Behavior Checklist (CBCL)-and one neuropsychological test (Integrated Visual and Auditory Continuous Performance Testing). The two-sample t-test (FDR correction) screened the parameters of the three assessment tools with significant intergroup differences. LibSVM was used to establish a classification prediction model for analysing the accuracy of ADHD prediction using parameters of the three assessment tools and weight values of each parameter for classification prediction. Results: A total of 19 parameters (16 from clinical rating scales, 3 from neuropsychological tests) with significant inter-group differences were screened. The accuracy of classification modelling was higher for the clinical rating scales (61.635%) than for the neuropsychological test (59.784%), whereas the accuracy of classification modelling was higher for the clinical rating scales combined with the neuropsychological test (70.440%) than for the former two parameters alone. The three parameters with the highest weight values were learning problem (0.731), hyperactivity/impulsivity (0.676) and activity capacity (0.569). The three parameters with the lowest weight values are integrated control force (0.028), visual attention (0.028) and integrated attention (0.034). Conclusion: Our study findings indicate that the diagnosis of ADHD should be based on multidimensional assessment. For a more accurate diagnosis of ADHD, assessments and that more assessment parameters should be developed on the basis of different dimensions of physiology or psychology in the future to obtain a more accurate diagnosis of ADHD. Furthermore, the predictive model for ADHD may improve our understanding and help in optimisation of the treatment of such a condition.

show abstract

Section: Discussionmentioning

confidence: 99%

<p>A Predictive Model Offor Attention Deficit Hyperactivity Disorder Based on Clinical Assessment Tools</p>

Han¹,

Fang²,

Hong³

2020

NDT

View full text Add to dashboard Cite

show abstract

“…The tasks presented were in studies that were organized into six groups: ER (15%), mental workload (MWL) (18%), MI (20%), SD (19%), sleep stage scoring (SS) (7%), and diagnosis of neurodegenerative diseases (ND), including Alzheimer’s disease (AD), Parkinson’s disease (PD), and schizophrenia (SZ) (9%). Other studies (12%) focused on ERP [ 47 , 149 , 150 , 151 , 152 ], anxiety and stress [ 153 , 154 ], depression [ 33 , 66 , 155 ], the detection of alcoholism [ 156 ], auditory diseases [ 157 ], attention deficit hyperactivity disorder [ 158 ], sleep apnea [ 159 ], and the classification of creativity [ 160 ]. In recent years, the application of supervised ML and DL models in MI and ER has gained significant attention, despite decades-long research studies already in progress in these fields ( Figure 5 ).…”

Section: Resultsmentioning

confidence: 99%

“… 6 channels Keirn and Aunon database WT/EMD SVM KNN Accuracy = 80–100 [ 184 ] 2020 SD 10 subj. BONN database FT/WT SVM KNN Accuracy = 100 [ 158 ] 2020 ADHD 97 subj. 19 channels Own database PSR NDC EPNN SVM Accuracy = 100 [ 177 ] 2020 MI 5 subj.…”

Section: Table A1mentioning

confidence: 99%

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

et al. 2021

View full text Add to dashboard Cite

Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly being applied to EEG data for pattern analysis, group membership classification, and brain-computer interface purposes. This study aimed to systematically review recent advances in ML and DL supervised models for decoding and classifying EEG signals. Moreover, this article provides a comprehensive review of the state-of-the-art techniques used for EEG signal preprocessing and feature extraction. To this end, several academic databases were searched to explore relevant studies from the year 2000 to the present. Our results showed that the application of ML and DL in both mental workload and motor imagery tasks has received substantial attention in recent years. A total of 75% of DL studies applied convolutional neural networks with various learning algorithms, and 36% of ML studies achieved competitive accuracy by using a support vector machine algorithm. Wavelet transform was found to be the most common feature extraction method used for all types of tasks. We further examined the specific feature extraction methods and end classifier recommendations discovered in this systematic review.

show abstract

“…The focus of this study was on ERP data because the event-related data are more sensitive and provide better performance than spontaneous EEG data recorded while the participants are at a resting state. Various studies have shown that patients with ADHD display brain alterations using ERP attributes (Müller et al 2019 ; Lenartowicz and Loo 2014 ; Öztoprak et al 2017 ; Li et al 2018b ; Kaur et al 2020 ). However, the pre-processing method may also improve the performance of EEG data.…”

Section: Discussionmentioning

confidence: 99%

Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials

2022

View full text Add to dashboard Cite

Accurate diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) is a significant challenge. Misdiagnosis has significant negative medical side effects. Due to the complex nature of this disorder, there is no computational expert system for diagnosis. Recently, automatic diagnosis of ADHD by machine learning analysis of brain signals has received an increased attention. This paper aimed to achieve an accurate model to discriminate between ADHD patients and healthy controls by pattern discovery. Event-Related Potentials (ERP) data were collected from ADHD patients and healthy controls. After pre-processing, ERP signals were decomposed and features were calculated for different frequency bands. The classification was carried out based on each feature using seven machine learning algorithms. Important features were then selected and combined. To find specific patterns for each model, the classification was repeated using the proposed patterns. Results indicated that the combination of complementary features can significantly improve the performance of the predictive models. The newly developed features, defined based on band power, were able to provide the best classification using the Generalized Linear Model, Logistic Regression, and Deep Learning with the average accuracy and Receiver operating characteristic curve > %99.85 and > 0.999, respectively. High and low frequencies (Beta, Delta) performed better than the mid, frequencies in the discrimination of ADHD from control. Altogether, this study developed a machine learning expert system that minimises misdiagnosis of ADHD and is beneficial for the evaluation of treatment efficacy. Graphical abstract

show abstract

Phase Space Reconstruction of EEG Signals for Classification of ADHD and Control Adults

Cited by 37 publications

References 45 publications

<p>A Predictive Model Offor Attention Deficit Hyperactivity Disorder Based on Clinical Assessment Tools</p>

<p>A Predictive Model Offor Attention Deficit Hyperactivity Disorder Based on Clinical Assessment Tools</p>

Neural Decoding of EEG Signals with Machine Learning: A Systematic Review

Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials

Contact Info

Product

Resources

About