Computational Decision Support System for ADHD Identification  

作　　者：Senuri De Silva Sanuwani Dayarathna Gangani Ariyarathne Dulani Meedeniya Sampath Jayarathna Anne M.P.Michalek 

机构地区：[1]Department of Computer Science and Engineering,University of Moratuwa,Moratuwa 10400,Sri Lanka [2]Department of Computer Science,College of Science,Old Dominion University,Norfolk 23529,USA [3]Department of Communication Disorders and Special Education,Old Dominion University,Norfolk 23529,USA

出　　处：《International Journal of Automation and computing》2021年第2期233-255,共23页国际自动化与计算杂志（英文版）

基　　金：This work was supported by Old Dominion University,Norfolk,Virginia,USA and University of Moratuwa,Sri Lanka.We thank the participants of the system usability study.

摘　　要：Attention deficit/hyperactivity disorder(ADHD)is a common disorder among children.ADHD often prevails into adulthood,unless proper treatments are facilitated to engage self-regulatory systems.Thus,there is a need for effective and reliable mechanisms for the early identification of ADHD.This paper presents a decision support system for the ADHD identification process.The proposed system uses both functional magnetic resonance imaging(fMRI)data and eye movement data.The classification processes contain enhanced pipelines,and consist of pre-processing,feature extraction,and feature selection mechanisms.fMRI data are processed by extracting seed-based correlation features in default mode network(DMN)and eye movement data using aggregated features of fixations and saccades.For the classification using eye movement data,an ensemble model is obtained with 81%overall accuracy.For the fMRI classification,a convolutional neural network(CNN)is used with 82%accuracy for the ADHD identification.Both ensemble models are proved for overfitting avoidance.

关 键 词：Attention deficit/hyperactivity disorder(ADHD) functional magnetic resonance imaging(fMRI) eye movement data seed-based correlation ensembled model convolutional neural network(CNN) default mode network(DMN) SACCADES FIXATIONS ADHD-Care decision support system(DDS) 

分 类 号：R749.94[医药卫生—神经病学与精神病学] TP315[医药卫生—临床医学]