Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals

Hui Tian Tor; Chui Ping Ooi; Nikki Sj Lim-Ashworth; Joel Koh En Wei; V Jahmunah; Shu Lih Oh; U Rajendra Acharya; Daniel Shuen Sheng Fung

doi:10.1016/j.cmpb.2021.105941

Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals

Comput Methods Programs Biomed. 2021 Mar:200:105941. doi: 10.1016/j.cmpb.2021.105941. Epub 2021 Jan 14.

Authors

Hui Tian Tor¹, Chui Ping Ooi¹, Nikki Sj Lim-Ashworth², Joel Koh En Wei³, V Jahmunah³, Shu Lih Oh³, U Rajendra Acharya⁴, Daniel Shuen Sheng Fung⁵

Affiliations

¹ School of Science and Technology, Singapore University of Social Sciences, Singapore.
² Developmental Psychiatry, Institute of Mental Health, Singapore.
³ School of Engineering, Ngee Ann Polytechnic, Singapore.
⁴ School of Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan, ROC; School of Management and Enterprise University of Southern Queensland, Springfield, Australia. Electronic address: aru@np.edu.sg.
⁵ Developmental Psychiatry, Institute of Mental Health, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University; DUKE NUS Medical School, National University of Singapore; Yong Loo Lin School of Medicine, National University of Singapore.

PMID: 33486340
DOI: 10.1016/j.cmpb.2021.105941

Abstract

Background and objectives: Attention deficit hyperactivity disorder (ADHD) is often presented with conduct disorder (CD). There is currently no objective laboratory test or diagnostic method to discern between ADHD and CD, and diagnosis is further made difficult as ADHD is a common neuro-developmental disorder often presenting with other co-morbid difficulties; and in particular with conduct disorder which has a high degree of associated behavioural challenges. A novel automated system (AS) is proposed as a convenient supplementary tool to support clinicians in their diagnostic decisions. To the best of our knowledge, we are the first group to develop an automated classification system to classify ADHD, CD and ADHD+CD classes using brain signals.

Methods: The empirical mode decomposition (EMD) and discrete wavelet transform (DWT) methods were employed to decompose the electroencephalogram (EEG) signals. Autoregressive modelling coefficients and relative wavelet energy were then computed on the signals. Various nonlinear features were extracted from the decomposed coefficients. Adaptive synthetic sampling (ADASYN) was then employed to balance the dataset. The significant features were selected using sequential forward selection method. The highly discriminatory features were subsequently fed to an array of classifiers.

Results: The highest accuracy of 97.88% was achieved with the K-Nearest Neighbour (KNN) classifier. The proposed system was developed using ten-fold validation strategy on EEG data from 123 children. To the best of our knowledge this is the first study to develop an AS for the classification of ADHD, CD and ADHD+CD classes using EEG signals.

Potential application: Our AS can potentially be used as a web-based application with cloud system to aid the clinical diagnosis of ADHD and/or CD, thus supporting faster and accurate treatment for the children. It is important to note that testing with larger data is required before the AS can be employed for clinical applications.

Keywords: Attention deficit hyperactive disorder; Classifiers; Conduct disorder; K-fold validation; Machine learning; Nonlinear features; Sequential forward selection.

MeSH terms

Attention Deficit Disorder with Hyperactivity* / diagnosis
Brain
Child
Conduct Disorder* / diagnosis
Electroencephalography
Humans
Wavelet Analysis