Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals

Koh, Joel En Wei; Ooi, Chui Ping; Lim-Ashworth, Nikki; Vicnesh, Jahmunah; Tor, Hui Tian; Lih, Oh Shu; Tan, Ru San; Acharya, U. Rajendra; Fung, Daniel

doi:10.1016/j.compbiomed.2021.105120

Cited by 49 publications

(23 citation statements)

References 57 publications

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“…Drawbacks: Parameters should be optimized to gain higher classification performances. Recently, authors in [ 37 ] have developed an automated system to detect ADHD and conduct disorder in children using empirical wavelet transform and entropy features extracted from electrocardiogram (ECG) signals. They obtained an accuracy of 88% in classifying ADHD, ADHD + CD, and CD patients for appropriate intervention using accessible ECG signals.…”

Section: Discussionmentioning

confidence: 99%

TMP19: A Novel Ternary Motif Pattern-Based ADHD Detection Model Using EEG Signals

et al. 2022

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Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental condition worldwide. In this research, we used an ADHD electroencephalography (EEG) dataset containing more than 4000 EEG signals. Moreover, these EEGs are noisy signals. A new hand-modeled EEG classification model has been proposed to separate healthy versus ADHD individuals using the EEG signals. In this model, a new ternary motif pattern (TMP) has been incorporated. We have mimicked deep learning networks to create this hand-modeled classification method. The Tunable Q Wavelet Transform (TQWT) has been utilized to generate wavelet subbands. We applied the proposed TMP and statistics to construct informative features from both raw EEG signals and wavelet bands by generating TQWT. Herein, features have been generated by 18 subbands and the original EEG signal. Thus, this model is named TMP19. The most informative features have been chosen by deploying neighborhood component analysis (NCA), and the selected features have been classified using the k-nearest neighbor (kNN) classifier. The used ADHD EEG dataset has 14 channels. Thus, these three phases—(i) feature extraction with TQWT, TMP, and statistics; (ii) feature selection by deploying NCA; and (iii) classification with kNN—have been applied to each channel. Iterative hard majority voting (IHMV) has been applied to obtain a higher and more general classification response. Our model attained 95.57% and 77.93% classification accuracies by deploying 10-fold and leave one subject out (LOSO) cross-validations, respectively.

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Section: Discussionmentioning

confidence: 99%

TMP19: A Novel Ternary Motif Pattern-Based ADHD Detection Model Using EEG Signals

et al. 2022

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“…Non-genetic risk factors have been suggested to include brain injury, premature delivery, maternal alcohol and tobacco use during pregnancy, and contact with certain environmental agents, such as lead, during pregnancy or at a young age [16][17][18]. Not uncommonly, children meeting diagnostic criteria for ADHD have other comorbidities, including oppositional defiant and conduct disorders (ODD and CDs) [19,20] and depressive and anxiety disorders [21], as well as specific learning disorders [22]. Children with ADHD have difficulties in maintaining sustained attention, can be hyperactive, fidget and find it difficult to participate in turn taking [17].…”

Section: Adhdmentioning

confidence: 99%

Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review

Barua

Vicnesh

Gururajan

et al. 2022

IJERPH

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Mental disorders (MDs) with onset in childhood or adolescence include neurodevelopmental disorders (NDDs) (intellectual disability and specific learning disabilities, such as dyslexia, attention deficit disorder (ADHD), and autism spectrum disorders (ASD)), as well as a broad range of mental health disorders (MHDs), including anxiety, depressive, stress-related and psychotic disorders. There is a high co-morbidity of NDDs and MHDs. Globally, there have been dramatic increases in the diagnosis of childhood-onset mental disorders, with a 2- to 3-fold rise in prevalence for several MHDs in the US over the past 20 years. Depending on the type of MD, children often grapple with social and communication deficits and difficulties adapting to changes in their environment, which can impact their ability to learn effectively. To improve outcomes for children, it is important to provide timely and effective interventions. This review summarises the range and effectiveness of AI-assisted tools, developed using machine learning models, which have been applied to address learning challenges in students with a range of NDDs. Our review summarises the evidence that AI tools can be successfully used to improve social interaction and supportive education. Based on the limitations of existing AI tools, we provide recommendations for the development of future AI tools with a focus on providing personalised learning for individuals with NDDs.

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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%

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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Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals

Cited by 49 publications

References 57 publications

TMP19: A Novel Ternary Motif Pattern-Based ADHD Detection Model Using EEG Signals

TMP19: A Novel Ternary Motif Pattern-Based ADHD Detection Model Using EEG Signals

Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review

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

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