Extreme Learning Machine-Based Classification of ADHD Using Brain Structural MRI Data
Top Cited Papers
Open Access
- 19 November 2013
- journal article
- research article
- Published by Public Library of Science (PLoS) in PLOS ONE
- Vol. 8 (11), e79476
- https://doi.org/10.1371/journal.pone.0079476
Abstract
Effective and accurate diagnosis of attention-deficit/hyperactivity disorder (ADHD) is currently of significant interest. ADHD has been associated with multiple cortical features from structural MRI data. However, most existing learning algorithms for ADHD identification contain obvious defects, such as time-consuming training, parameters selection, etc. The aims of this study were as follows: (1) Propose an ADHD classification model using the extreme learning machine (ELM) algorithm for automatic, efficient and objective clinical ADHD diagnosis. (2) Assess the computational efficiency and the effect of sample size on both ELM and support vector machine (SVM) methods and analyze which brain segments are involved in ADHD. High-resolution three-dimensional MR images were acquired from 55 ADHD subjects and 55 healthy controls. Multiple brain measures (cortical thickness, etc.) were calculated using a fully automated procedure in the FreeSurfer software package. In total, 340 cortical features were automatically extracted from 68 brain segments with 5 basic cortical features. F-score and SFS methods were adopted to select the optimal features for ADHD classification. Both ELM and SVM were evaluated for classification accuracy using leave-one-out cross-validation. We achieved ADHD prediction accuracies of 90.18% for ELM using eleven combined features, 84.73% for SVM-Linear and 86.55% for SVM-RBF. Our results show that ELM has better computational efficiency and is more robust as sample size changes than is SVM for ADHD classification. The most pronounced differences between ADHD and healthy subjects were observed in the frontal lobe, temporal lobe, occipital lobe and insular. Our ELM-based algorithm for ADHD diagnosis performs considerably better than the traditional SVM algorithm. This result suggests that ELM may be used for the clinical diagnosis of ADHD and the investigation of different brain diseases.Keywords
This publication has 52 references indexed in Scilit:
- A Novel Approach for Lie Detection Based on F-Score and Extreme Learning MachinePLOS ONE, 2013
- Sparse Representation of Brain Aging: Extracting Covariance Patterns from Structural MRIPLOS ONE, 2012
- Using Support Vector Machines with Multiple Indices of Diffusion for Automated Classification of Mild Cognitive ImpairmentPLOS ONE, 2012
- Towards a Physiology-Based Measure of Pain: Patterns of Human Brain Activity Distinguish Painful from Non-Painful Thermal StimulationPLOS ONE, 2011
- Multimodal classification of Alzheimer's disease and mild cognitive impairmentNeuroImage, 2011
- Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion ImagingPLOS ONE, 2010
- The importance of relative standards in ADHD diagnoses: Evidence based on exact birth datesJournal of Health Economics, 2010
- A Neuronal Basis for Task-Negative Responses in the Human BrainCerebral Cortex, 2010
- Abnormal cerebral cortex structure in children with ADHDHuman Brain Mapping, 2007
- Permutation Tests for Classification: Towards Statistical Significance in Image-Based StudiesLecture Notes in Computer Science, 2003