1
|
Feng M, Xu J. Electroencephalogram-Based ConvMixer Architecture for Recognizing Attention Deficit Hyperactivity Disorder in Children. Brain Sci 2024; 14:469. [PMID: 38790448 PMCID: PMC11118831 DOI: 10.3390/brainsci14050469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 04/29/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Min Feng
- Nanjing Rehabilitation Medical Center, The Affiliated Brain Hospital, Nanjing Medical University, Nanjing 210029, China
- School of Chinese Language and Literature, Nanjing Normal University, Nanjing 210024, China
| | - Juncai Xu
- School of Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| |
Collapse
|
2
|
Bayat M, Hernandez M, Curzon M, Garic D, Graziano P, Dick AS. Reduced recruitment of inhibitory control regions in very young children with ADHD during a modified Kiddie Continuous Performance Task: a fMRI study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.17.576033. [PMID: 38293209 PMCID: PMC10827162 DOI: 10.1101/2024.01.17.576033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Attention-Deficit/Hyperactivity Disorder (ADHD) symptom profiles are known to undergo changes throughout development, rendering the neurobiological assessment of ADHD challenging across different developmental stages. Particularly in young children (ages 4 to 7 years), measuring inhibitory control network activity in the brain has been a formidable task due to the lack of child-friendly functional Magnetic Resonance Imaging (fMRI) paradigms. This study aims to address these difficulties by focusing on measuring inhibitory control in very young children within the MRI environment. A total of 56 children diagnosed with ADHD and 78 typically developing (TD) 4-7-year-old children were examined using a modified version of the Kiddie-Continuous Performance Test (K-CPT) during BOLD fMRI to assess inhibitory control. We concurrently evaluated their performance on the established and standardized K-CPT outside the MRI scanner. Our findings suggest that the modified K-CPT effectively elicited robust and expected brain activity related to inhibitory control in both groups. Comparisons between the two groups revealed subtle differences in brain activity, primarily observed in regions associated with inhibitory control, such as the inferior frontal gyrus, anterior insula, dorsal striatum, medial pre-supplementary motor area (pre-SMA), and cingulate cortex. Notably, increased activity in the right anterior insula was associated with improved response time (RT) and reduced RT variability on the K-CPT administered outside the MRI environment, although this did not survive statistical correction for multiple comparisons. In conclusion, our study successfully overcame the challenges of measuring inhibitory control in very young children within the MRI environment by utilizing a modified K-CPT during BOLD fMRI. These findings shed light on the neurobiological correlates of inhibitory control in ADHD and TD children, provide valuable insights for understanding ADHD across development, and potentially inform ADHD diagnosis and intervention strategies. The research also highlights remaining challenges with task fMRI in very young clinical samples.
Collapse
|
3
|
He Y, Wang X, Yang Z, Xue L, Chen Y, Ji J, Wan F, Mukhopadhyay SC, Men L, Tong MCF, Li G, Chen S. Classification of attention deficit/hyperactivity disorder based on EEG signals using a EEG-Transformer model ∗. J Neural Eng 2023; 20:056013. [PMID: 37683665 DOI: 10.1088/1741-2552/acf7f5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 09/08/2023] [Indexed: 09/10/2023]
Abstract
Objective. Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in adolescents that can seriously impair a person's attention function, cognitive processes, and learning ability. Currently, clinicians primarily diagnose patients based on the subjective assessments of the Diagnostic and Statistical Manual of Mental Disorders-5, which can lead to delayed diagnosis of ADHD and even misdiagnosis due to low diagnostic efficiency and lack of well-trained diagnostic experts. Deep learning of electroencephalogram (EEG) signals recorded from ADHD patients could provide an objective and accurate method to assist physicians in clinical diagnosis.Approach. This paper proposes the EEG-Transformer deep learning model, which is based on the attention mechanism in the traditional Transformer model, and can perform feature extraction and signal classification processing for the characteristics of EEG signals. A comprehensive comparison was made between the proposed transformer model and three existing convolutional neural network models.Main results. The results showed that the proposed EEG-Transformer model achieved an average accuracy of 95.85% and an average AUC value of 0.9926 with the fastest convergence speed, outperforming the other three models. The function and relationship of each module of the model are studied by ablation experiments. The model with optimal performance was identified by the optimization experiment.Significance. The EEG-Transformer model proposed in this paper can be used as an auxiliary tool for clinical diagnosis of ADHD, and at the same time provides a basic model for transferable learning in the field of EEG signal classification.
Collapse
Affiliation(s)
- Yuchao He
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, People's Republic of China
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, Guangdong 518055, People's Republic of China
| | - Xin Wang
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, People's Republic of China
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, Guangdong 518055, People's Republic of China
| | - Zijian Yang
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, People's Republic of China
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, Guangdong 518055, People's Republic of China
| | - Lingbin Xue
- Department of Otorhinolaryngology, Head and Neck Surgery, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China 000000, People's Republic of China
| | - Yuming Chen
- School of Psychology, Shenzhen University, Shenzhen 518060, People's Republic of China
| | - Junyu Ji
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, People's Republic of China
- Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong 518055, People's Republic of China
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, Guangdong 518055, People's Republic of China
| | - Feng Wan
- Faculty of Science and Technology, University of Macau, Macau 999078, People's Republic of China
| | | | - Lina Men
- Department of Neonatology, Shenzhen Children's Hospital, Shenzhen 518034, People's Republic of China
| | - Michael Chi Fai Tong
- Department of Otorhinolaryngology, Head and Neck Surgery, The Chinese University of Hong Kong, Hong Kong Special Administrative Region of China 000000, People's Republic of China
| | - Guanglin Li
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, People's Republic of China
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, Guangdong 518055, People's Republic of China
| | - Shixiong Chen
- CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, People's Republic of China
- Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen, Guangdong 518055, People's Republic of China
| |
Collapse
|
4
|
Lee W, Lee D, Lee S, Jun K, Kim MS. Deep-Learning-Based ADHD Classification Using Children's Skeleton Data Acquired through the ADHD Screening Game. SENSORS (BASEL, SWITZERLAND) 2022; 23:246. [PMID: 36616844 PMCID: PMC9824773 DOI: 10.3390/s23010246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 12/19/2022] [Accepted: 12/21/2022] [Indexed: 06/17/2023]
Abstract
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%.
Collapse
|
5
|
Kohli M, Kar AK, Bangalore A, AP P. Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study. Brain Inform 2022; 9:16. [PMID: 35879626 PMCID: PMC9311349 DOI: 10.1186/s40708-022-00164-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 06/25/2022] [Indexed: 12/27/2022] Open
Abstract
Autism spectrum is a brain development condition that impairs an individual's capacity to communicate socially and manifests through strict routines and obsessive-compulsive behavior. Applied behavior analysis (ABA) is the gold-standard treatment for autism spectrum disorder (ASD). However, as the number of ASD cases increases, there is a substantial shortage of licensed ABA practitioners, limiting the timely formulation, revision, and implementation of treatment plans and goals. Additionally, the subjectivity of the clinician and a lack of data-driven decision-making affect treatment quality. We address these obstacles by applying two machine learning algorithms to recommend and personalize ABA treatment goals for 29 study participants with ASD. The patient similarity and collaborative filtering methods predicted ABA treatment with an average accuracy of 81-84%, with a normalized discounted cumulative gain of 79-81% (NDCG) compared to clinician-prepared ABA treatment recommendations. Additionally, we assess the two models' treatment efficacy (TE) by measuring the percentage of recommended treatment goals mastered by the study participants. The proposed treatment recommendation and personalization strategy are generalizable to other intervention methods in addition to ABA and for other brain disorders. This study was registered as a clinical trial on November 5, 2020 with trial registration number CTRI/2020/11/028933.
Collapse
Affiliation(s)
- Manu Kohli
- Indian Institute of Technology-Delhi, Department of Management Studies, IV Floor, Vishwakarma Bhavan, Shaheed Jeet Singh Marg, Hauz Khas, New Delhi, 110016 India
| | - Arpan Kumar Kar
- Indian Institute of Technology-Delhi, Department of Management Studies, IV Floor, Vishwakarma Bhavan, Shaheed Jeet Singh Marg, Hauz Khas, New Delhi, 110016 India
| | - Anjali Bangalore
- ICON Centre, K. M. Chavan chawk, Shivajinagar Road, Garkheda, Aurangabad, 431005 India
| | - Prathosh AP
- Indian Institute of Science, CV Raman Rd, Bengaluru, 560012 Karnataka India
| |
Collapse
|
6
|
Chen IC, Chang CL, Chang MH, Ko LW. Atypical functional connectivity during rest and task-related dynamic alteration in young children with attention deficit hyperactivity disorder: An analysis using the phase-locking value. Psychiatry Clin Neurosci 2022; 76:235-245. [PMID: 35235255 DOI: 10.1111/pcn.13344] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 02/18/2022] [Accepted: 02/24/2022] [Indexed: 11/30/2022]
Abstract
AIM The study investigated the electroencephalography (EEG) functional connectivity (FC) profiles during rest and tasks of young children with attention deficit hyperactivity disorder (ADHD) and typical development (TD). METHODS In total, 78 children (aged 5-7 years) were enrolled in this study; 43 of them were diagnosed with ADHD and 35 exhibited TD. Four FC metrics, coherence, phase-locking value (PLV), pairwise phase consistency, and phase lag index, were computed for feature selection to discriminate ADHD from TD. RESULTS The support vector machine classifier trained by phase-locking value (PLV) features yielded the best performance to differentiate the ADHD from the TD group and was used for further analysis. In comparing PLVs with the TD group at rest, the ADHD group exhibited significantly lower values on left intrahemispheric long interelectrode lower-alpha and beta as well as frontal interhemispheric beta frequency bands. However, the ADHD group showed higher values of central interhemispheric PLVs on the theta, higher-alpha, and beta bands. Regarding PLV alterations within resting and task conditions, left intrahemispheric long interelectrode beta PLVs declined from rest to task in the TD group, but the alterations did not differ in the ADHD group. Negative correlations were observed between frontal interhemispheric beta PLVs and the Disruptive Behavior Disorder Rating Scale as rated by teachers. CONCLUSIONS These results, which complement the findings of other sparse studies that have investigated task-related brain FC dynamics, particularly in young children with ADHD, can provide clinicians with significant and interpretable neural biomarkers for facilitating the diagnosis of ADHD.
Collapse
Affiliation(s)
- I-Chun Chen
- International Ph. D. Program in Interdisciplinary Neuroscience, College of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.,Department of Physical Medicine and Rehabilitation, Ton-Yen General Hospital, Hsinchu, Taiwan
| | | | - Meng-Han Chang
- Department of Psychiatry, Ton-Yen General Hospital, Hsinchu, Taiwan
| | - Li-Wei Ko
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.,Department of Electrical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.,Brain Research Center and the Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu, Taiwan.,Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung, Taiwan
| |
Collapse
|
7
|
Chen IC, Chen CL, Chang CH, Fan ZC, Chang Y, Lin CH, Ko LW. Task-Rate-Related Neural Dynamics Using Wireless EEG to Assist Diagnosis and Intervention Planning for Preschoolers with ADHD Exhibiting Heterogeneous Cognitive Proficiency. J Pers Med 2022; 12:jpm12050731. [PMID: 35629153 PMCID: PMC9143733 DOI: 10.3390/jpm12050731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 04/21/2022] [Accepted: 04/25/2022] [Indexed: 02/01/2023] Open
Abstract
This study used a wireless EEG system to investigate neural dynamics in preschoolers with ADHD who exhibited varying cognitive proficiency pertaining to working memory and processing speed abilities. Preschoolers with ADHD exhibiting high cognitive proficiency (ADHD-H, n = 24), those with ADHD exhibiting low cognitive proficiency (ADHD-L, n = 18), and preschoolers with typical development (TD, n = 31) underwent the Conners’ Kiddie Continuous Performance Test and wireless EEG recording under different conditions (rest, slow-rate, and fast-rate task). In the slow-rate task condition, compared with the TD group, the ADHD-H group manifested higher delta and lower beta power in the central region, while the ADHD-L group manifested higher parietal delta power. In the fast-rate task condition, in the parietal region, ADHD-L manifested higher delta power than those in the other two groups (ADHD-H and TD); additionally, ADHD-L manifested higher theta as well as lower alpha and beta power than those with ADHD-H. Unlike those in the TD group, the delta power of both ADHD groups was enhanced in shifting from rest to task conditions. These findings suggest that task-rate-related neural dynamics contain specific neural biomarkers to assist clinical planning for ADHD in preschoolers with heterogeneous cognitive proficiency. The novel wireless EEG system used was convenient and highly suitable for clinical application.
Collapse
Affiliation(s)
- I-Chun Chen
- International Ph.D. Program in Interdisciplinary Neuroscience, College of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan;
- Department of Physical Medicine and Rehabilitation, Ton-Yen General Hospital, Hsinchu 30268, Taiwan
| | - Chia-Ling Chen
- Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital, Linkou, Taoyuan 33305, Taiwan
- Graduate Institute of Early Intervention, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
- Correspondence: (C.-L.C.); (L.-W.K.)
| | - Chih-Hao Chang
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan; (C.-H.C.); (Z.-C.F.)
- Brain Research Center and the Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan;
| | - Zuo-Cian Fan
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan; (C.-H.C.); (Z.-C.F.)
- Brain Research Center and the Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan;
| | - Yang Chang
- Brain Research Center and the Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan;
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
| | | | - Li-Wei Ko
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan; (C.-H.C.); (Z.-C.F.)
- Brain Research Center and the Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan;
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
- Department of Electronics and Electrical Engineering, Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
- Drug Development and Value Creation Research Center, Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Correspondence: (C.-L.C.); (L.-W.K.)
| |
Collapse
|