Abstract
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.
Highlights
A TTENTION deficit/Hyperactivity disorder (ADHD) is a neurodevelopmental condition characterized by core symptoms such as inattention, hyperactivity, and impulsivity [1]
To solve the problem of ADHD classification based on functional magnetic resonance imaging (fMRI) images, the convolution denoising autoencoder is proposed as the feature extractor in the feature extraction stage
Considering the small amount of fMRI image data in the ADHD-200 dataset, we utilized the fMRI spatial features extracted in time order to perform dimension reduction processing again based on principal component analysis (PCA) to avoid over-fitting caused by “small sample and high-dimension”
Summary
A TTENTION deficit/Hyperactivity disorder (ADHD) is a neurodevelopmental condition characterized by core symptoms such as inattention, hyperactivity, and impulsivity [1]. Zou et al [12], proposed a 3D-convolutional neural network (CNN) deep learning classification method based on fMRI and sMRI. The proposed 4D-CNN extracting the spatial and time information of fMRI at the same time achieved the highest accuracy of 71.3% in the application to ADHD classification. To solve the problem of ADHD classification based on fMRI images, the convolution denoising autoencoder is proposed as the feature extractor in the feature extraction stage. Considering the small amount of fMRI image data in the ADHD-200 dataset, we utilized the fMRI spatial features extracted in time order to perform dimension reduction processing again based on principal component analysis (PCA) to avoid over-fitting caused by “small sample and high-dimension”. The remainder of this article is arranged as follows: the second section introduces the theoretical background of the proposed CDAE-AdaDT algorithm in detail; the third section is the experimental setup, including data processing and training details of the CDAE-AdaDT algorithm model; the fourth section describes and discusses the experimental results; the last section summarizes the algorithm and experimental results of this article
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
