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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Vydáno v:	IEEE transactions on neural systems and rehabilitation engineering Ročník 29; s. 1 - 10
Hlavní autoři:	Liu, Shuaiqi, Zhao, Ling, Wang, Xu, Xin, Qi, Zhao, Jie, Guttery, David S., Zhang, Yu-Dong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1534-4320 1558-0210 1558-0210
DOI:	10.1109/TNSRE.2020.3019063