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Deep neural network technique for automated detection of ADHD and CD using ECG signal

Loh HW 1 , Ooi CP 1 , Oh SL 2 , Barua PD 3 , Tan YR 4 , Molinari F 5 Show all authors , March S 6 , Acharya UR 7 , Fung DSS 8

Affiliations 

  • 1 School of Science and Technology, Singapore University of Social Sciences, Singapore
  • 2 Cogninet Australia, Sydney, NSW 2010, Australia
  • 3 Cogninet Australia, Sydney, NSW 2010, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia; School of Business (Information System), University of Southern Queensland, Australia; Australian International Institute of Higher Education, Sydney, NSW 2000, Australia; School of Science & Technology, University of New England, Australia; School of Biosciences, Taylor's University, Malaysia; School of Computing, SRM Institute of Science and Technology, India; School of Science and Technology, Kumamoto University, Japan; Sydney School of Education and Social work, University of Sydney, Australia
  • 4 Developmental Psychiatry, Institute of Mental Health, Singapore
  • 5 Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy
  • 6 Centre for Health Research and School of Psychology and Wellbeing, University of Southern Queensland, Springfield, Australia
  • 7 School of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, Australia. Electronic address: rajendra.acharya@usq.edu.au
  • 8 Developmental Psychiatry, Institute of Mental Health, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, DUKE NUS Medical School, Yong Loo Lin School of Medicine, National University of Singapore
Comput Methods Programs Biomed, 2023 Nov;241:107775.
PMID: 37651817 DOI: 10.1016/j.cmpb.2023.107775

Abstract

BACKGROUND AND OBJECTIVE: Attention Deficit Hyperactivity problem (ADHD) is a common neurodevelopment problem in children and adolescents that can lead to long-term challenges in life outcomes if left untreated. Also, ADHD is frequently associated with Conduct Disorder (CD), and multiple research have found similarities in clinical signs and behavioral symptoms between both diseases, making differentiation between ADHD, ADHD comorbid with CD (ADHD+CD), and CD a subjective diagnosis. Therefore, the goal of this pilot study is to create the first explainable deep learning (DL) model for objective ECG-based ADHD/CD diagnosis as having an objective biomarker may improve diagnostic accuracy.

METHODS: The dataset used in this study consist of ECG data collected from 45 ADHD, 62 ADHD+CD, and 16 CD patients at the Child Guidance Clinic in Singapore. The ECG data were segmented into 2 s epochs and directly used to train our 1-dimensional (1D) convolutional neural network (CNN) model.

RESULTS: The proposed model yielded 96.04% classification accuracy, 96.26% precision, 95.99% sensitivity, and 96.11% F1-score. The Gradient-weighted class activation mapping (Grad-CAM) function was also used to highlight the important ECG characteristics at specific time points that most impact the classification score.

CONCLUSION: In addition to achieving model performance results with our suggested DL method, Grad-CAM's implementation also offers vital temporal data that clinicians and other mental healthcare professionals can use to make wise medical judgments. We hope that by conducting this pilot study, we will be able to encourage larger-scale research with a larger biosignal dataset. Hence allowing biosignal-based computer-aided diagnostic (CAD) tools to be implemented in healthcare and ambulatory settings, as ECG can be easily obtained via wearable devices such as smartwatches.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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