Diagnosis of Attention Deficit Hyperactivity Disorder with combined time and frequency features

Altınkaynak, Miray; Dolu, Nazan; Güven, Ayşegül; Pektaş, Ferhat; Özmen, Sevgi; Demirci, Esra; İzzetoğlu, Meltem

doi:10.1016/j.bbe.2020.04.006

Cited by 53 publications

(35 citation statements)

References 39 publications

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“…The second example comprises studies on the diagnosis of ADHD using EEG data [ 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. The team of Tosun et al obtained 92.2% ADHD classification accuracy using an LSTM-based deep learning algorithm for 1088 ADHD patients and 1088 normal groups [ 25 ].…”

Section: Related Workmentioning

confidence: 99%

“…The team of Tosun et al obtained 92.2% ADHD classification accuracy using an LSTM-based deep learning algorithm for 1088 ADHD patients and 1088 normal groups [ 25 ]. In addition, the research team of Altinkaynak et al obtained an accuracy of 91.3% using an MLP-based machine learning algorithm using EEG data of 23 ADHD patients and 23 normal subjects [ 27 ].…”

Section: Related Workmentioning

confidence: 99%

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Deep-Learning-Based ADHD Classification Using Children’s Skeleton Data Acquired through the ADHD Screening Game

Lee

et al. 2022

Sensors

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The identification of attention deficit hyperactivity disorder (ADHD) in children, which is increasing every year worldwide, is very important for early diagnosis and treatment. However, since ADHD is not a simple disease that can be diagnosed with a simple test, doctors require a large period of time and substantial effort for accurate diagnosis and treatment. Currently, ADHD classification studies using various datasets and machine learning or deep learning algorithms are actively being conducted for the screening diagnosis of ADHD. However, there has been no study of ADHD classification using only skeleton data. It was hypothesized that the main symptoms of ADHD, such as distraction, hyperactivity, and impulsivity, could be differentiated through skeleton data. Thus, we devised a game system for the screening and diagnosis of children’s ADHD and acquired children’s skeleton data using five Azure Kinect units equipped with depth sensors, while the game was being played. The game for screening diagnosis involves a robot first travelling on a specific path, after which the child must remember the path the robot took and then follow it. The skeleton data used in this study were divided into two categories: standby data, obtained when a child waits while the robot demonstrates the path; and game data, obtained when a child plays the game. The acquired data were classified using the RNN series of GRU, RNN, and LSTM algorithms; a bidirectional layer; and a weighted cross-entropy loss function. Among these, an LSTM algorithm using a bidirectional layer and a weighted cross-entropy loss function obtained a classification accuracy of 97.82%.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Deep-Learning-Based ADHD Classification Using Children’s Skeleton Data Acquired through the ADHD Screening Game

Lee

et al. 2022

Sensors

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“…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. Brief comparison of studies on classification for ADHD is given in Table 6.…”

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 symptoms of ADHD develop in preschoolers and become more acute problems when they appear in school-aged children [2,3]. These symptoms have a negative impact on their academic activities, personal activities, and social activities, which last until adulthood [4,5]. Approximately 11% of children suffer from ADHD worldwide [3,6].…”

Section: Introductionmentioning

confidence: 99%

“…The ERP trial averaging method was used to evaluate children with ADHD from EEG data in the time domain. Moreover, morphological features were used to analyze ADHD subjects in several studies [5,6]. The power of various EEGbased frequency bands was used to make a diagnosis of children with ADHD.…”

Section: Introductionmentioning

confidence: 99%

Efficient Feature Selection and Machine Learning Based ADHD Detection Using EEG Signal

Maniruzzaman¹,

Shin²,

Hasan³

et al. 2022

Computers, Materials &Amp; Continua

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Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric and neurobehavioral disorders in children, affecting 11% of children worldwide. This study aimed to propose a machine learning (ML)-based algorithm for discriminating ADHD from healthy children using their electroencephalography (EEG) signals. The study included 61 children with ADHD and 60 healthy children aged 7-12 years. Different morphological and time-domain features were extracted from EEG signals. The t-test (p-value < 0.05) and least absolute shrinkage and selection operator (LASSO) were used to select potential features of children with ADHD and enhance the classification accuracy. The selected potential features were used in four ML-based algorithms, including support vector machine (SVM), k-nearest neighbors, multilayer perceptron (MLP), and logistic regression, to classify ADHD and healthy children. The overall prevalence of boys and girls with ADHD was 48.9% and 56.5%, respectively. The average age of children with ADHD was 9.6±1.8 years. Our results illustrated that the combination of LASSO with SVM classifier achieved the highest accuracy of 94.2%, sensitivity of 93.3%, F1-score of 91.9%, and AUC of 0.964. Our results also illustrated that MLP was the second-best ML-based classifier, which gave 93.4% accuracy, 91.7% sensitivity, 91.1% F1-score, and 0.960 AUC. The findings indicated that the combination of the LASSO-based feature selection method and SVM classifier can be a useful tool for selecting reliable/potential features and classifying ADHD and healthy children. Our proposed ML-based algorithms could be useful for the early diagnosis of children with ADHD.

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Diagnosis of Attention Deficit Hyperactivity Disorder with combined time and frequency features

Cited by 53 publications

References 39 publications

Deep-Learning-Based ADHD Classification Using Children’s Skeleton Data Acquired through the ADHD Screening Game

Deep-Learning-Based ADHD Classification Using Children’s Skeleton Data Acquired through the ADHD Screening Game

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

Efficient Feature Selection and Machine Learning Based ADHD Detection Using EEG Signal

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