TY - JOUR
T1 - Fusion of Multi-Task Neurophysiological Data to Enhance the Detection of Attention-Deficit/Hyperactivity Disorder
AU - Zhang, Kai Feng
AU - Yeh, Shih Ching
AU - Wu, Eric Hsiao Kuang
AU - Xu, Xiu
AU - Tsai, Ho Jung
AU - Chen, Chun Chuan
N1 - Publisher Copyright:
© 2024 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
PY - 2024
Y1 - 2024
N2 - Objective: Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder with a prevalence ranging from 6.1 to 9.4%. The main symptoms of ADHD are inattention, hyperactivity, impulsivity, and even destructive behaviors that may have a long-term negative influence on learning performance or social relationships. Early diagnosis and treatment provide the best chance of reducing and managing symptoms. Currently, ADHD diagnosis relies on behavioral observations and ratings by clinicians and parents. Medical diagnosis of ADHD was reported to be delayed because of a global shortage of well-trained clinicians, the heterogeneous nature of ADHD, and combined comorbidities. Therefore, alternative ways to increase the efficiency of early diagnosis are needed. Previous studies used behavioral and neurophysiological data to assess patients with ADHD, yielding an accuracy range from 56.6% to 92%. Several factors were shown to affect the detection rate, including methods and tasks used and the number of electroencephalogram (EEG) channels. Given that children with ADHD have difficulty sustaining attention, in this study, we tested whether data from multiple tasks with different difficulties and prolonged experiment times can probe the levels of brain resources engaged during task performance and increase ADHD detection. Specifically, we proposed a Deep Neural Network-based (DNN) fusion model of multiple tasks to enhance the detection of ADHD. Methods & Results: Forty-nine children with ADHD and thirty-two typically developing children were recruited. Analytic results show that the fusion of multi-task neurophysiological data can increase the separation rate to 89%, whereas a single data type can only achieve a best accuracy of 81%. Moreover, the use of multiple tasks helps distinguish between children with ADHD and typically developing children. Our results suggest that different neurophysiological models from multiple tasks can provide essential information to assist in ADHD screening. In conclusion, the proposed model offers a more efficient, and accurate alternative for early clinical diagnosis and management of ADHD. The application of artificial intelligence and multimodal neurophysiological data in clinical settings sets a precedent for digital health, paving the way for future advancements in the field.
AB - Objective: Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder with a prevalence ranging from 6.1 to 9.4%. The main symptoms of ADHD are inattention, hyperactivity, impulsivity, and even destructive behaviors that may have a long-term negative influence on learning performance or social relationships. Early diagnosis and treatment provide the best chance of reducing and managing symptoms. Currently, ADHD diagnosis relies on behavioral observations and ratings by clinicians and parents. Medical diagnosis of ADHD was reported to be delayed because of a global shortage of well-trained clinicians, the heterogeneous nature of ADHD, and combined comorbidities. Therefore, alternative ways to increase the efficiency of early diagnosis are needed. Previous studies used behavioral and neurophysiological data to assess patients with ADHD, yielding an accuracy range from 56.6% to 92%. Several factors were shown to affect the detection rate, including methods and tasks used and the number of electroencephalogram (EEG) channels. Given that children with ADHD have difficulty sustaining attention, in this study, we tested whether data from multiple tasks with different difficulties and prolonged experiment times can probe the levels of brain resources engaged during task performance and increase ADHD detection. Specifically, we proposed a Deep Neural Network-based (DNN) fusion model of multiple tasks to enhance the detection of ADHD. Methods & Results: Forty-nine children with ADHD and thirty-two typically developing children were recruited. Analytic results show that the fusion of multi-task neurophysiological data can increase the separation rate to 89%, whereas a single data type can only achieve a best accuracy of 81%. Moreover, the use of multiple tasks helps distinguish between children with ADHD and typically developing children. Our results suggest that different neurophysiological models from multiple tasks can provide essential information to assist in ADHD screening. In conclusion, the proposed model offers a more efficient, and accurate alternative for early clinical diagnosis and management of ADHD. The application of artificial intelligence and multimodal neurophysiological data in clinical settings sets a precedent for digital health, paving the way for future advancements in the field.
KW - Attention-deficit/hyperactivity disorder
KW - assessment
KW - deep learning
KW - multi-model fusion
KW - virtual reality
UR - http://www.scopus.com/inward/record.url?scp=85200262634&partnerID=8YFLogxK
U2 - 10.1109/JTEHM.2024.3435553
DO - 10.1109/JTEHM.2024.3435553
M3 - 期刊論文
C2 - 39464626
AN - SCOPUS:85200262634
SN - 2168-2372
VL - 12
SP - 668
EP - 674
JO - IEEE Journal of Translational Engineering in Health and Medicine
JF - IEEE Journal of Translational Engineering in Health and Medicine
ER -