Deep Spatio-Temporal Representation and Ensemble Classification for Attention Deficit/Hyperactivity Disorder

Attention deficit/Hyperactivity disorder (ADHD) is a complex, universal and heterogeneous neurodevelopmental disease. The traditional diagnosis of ADHD relies on the long-term analysis of complex information such as clinical data (electroencephalogram, etc.), patients' behavior and psychologica...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering Vol. 29; pp. 1 - 10
Main Authors: Liu, Shuaiqi, Zhao, Ling, Wang, Xu, Xin, Qi, Zhao, Jie, Guttery, David S., Zhang, Yu-Dong
Format: Journal Article
Language:English
Published: United States IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptive boosting decision tree
ADHD
Algorithms
Artificial neural networks
Attention deficit hyperactivity disorder
Brain mapping
Classification
Classification algorithms
Cognition
Convolution
convolutional denoising autoencoder
Data mining
Decision trees
EEG
Feature extraction
fMRI classification
Functional magnetic resonance imaging
Machine learning
Magnetic resonance imaging
Neural networks
Neurodevelopmental disorders
Neuroimaging
Noise reduction
Physicians
Psychological assessment
Psychological tests
Radiation
Spatial data
ISSN:1534-4320, 1558-0210, 1558-0210
Online Access:Get full text
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Summary:Attention deficit/Hyperactivity disorder (ADHD) is a complex, universal and heterogeneous neurodevelopmental disease. The traditional diagnosis of ADHD relies on the long-term analysis of complex information such as clinical data (electroencephalogram, etc.), patients' behavior and psychological tests by professional doctors. In recent years, functional magnetic resonance imaging (fMRI) has been developing rapidly and is widely employed in the study of brain cognition due to its non-invasive and non-radiation characteristics. We propose an algorithm based on convolutional denoising autoencoder (CDAE) and adaptive boosting decision trees (AdaDT) to improve the results of ADHD classification. Firstly, combining the advantages of convolutional neural networks (CNNs) and the denoising autoencoder (DAE), we developed a convolutional denoising autoencoder to extract the spatial features of fMRI data and obtain spatial features sorted by time. Then, AdaDT was exploited to classify the features extracted by CDAE. Finally, we validate the algorithm on the ADHD-200 test dataset. The experimental results show that our method offers improved classification compared with state-of-the-art methods in terms of the average accuracy of each individual site and all sites, meanwhile, our algorithm can maintain a certain balance between specificity and sensitivity.
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ISSN:1534-4320
1558-0210
1558-0210
DOI:10.1109/TNSRE.2020.3019063