Neurological state changes indicative of ADHD in children learned via EEG-based LSTM networks

Yang Chang; Cory Stevenson; I-Chun Chen; Dar-Shong Lin; Li-Wei Ko

doi:10.1088/1741-2552/ac4f07

Neurological state changes indicative of ADHD in children learned via EEG-based LSTM networks

J Neural Eng. 2022 Feb 10;19(1). doi: 10.1088/1741-2552/ac4f07.

Authors

Yang Chang¹, Cory Stevenson¹, I-Chun Chen^{2

3}, Dar-Shong Lin^{4

5}, Li-Wei Ko^{1

2

6

7}

Affiliations

¹ Institute of Bioinformatics and Systems Biology, and Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) in College of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
² International Ph. D. Program in Interdisciplinary Neuroscience in College of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
³ Department of Physical Medicine and Rehabilitation, Ton-Yen General Hospital, Hsinchu, Taiwan.
⁴ Department of Pediatrics, Mackay Memorial Hospital, Taipei, Taiwan.
⁵ Department of Medicine, Mackay Medical College, New Taipei, Taiwan.
⁶ Department of Electrical and Computer Engineering, and Brain Research Center, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
⁷ Drug Development and Value Creation Research Center, and Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung, Taiwan.

PMID: 35081524
DOI: 10.1088/1741-2552/ac4f07

Abstract

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that pervasively interferes with the lives of individuals starting in childhood.Objective. To address the subjectivity of current diagnostic approaches, many studies have been dedicated to efforts to identify the differences between ADHD and neurotypical (NT) individuals using electroencephalography (EEG) and continuous performance tests (CPT).Approach. In this study, we proposed EEG-based long short-term memory (LSTM) networks that utilize deep learning techniques with learning the cognitive state transition to discriminate between ADHD and NT children via EEG signal processing. A total of 30 neurotypical children and 30 ADHD children participated in CPT tests while being monitored with EEG. Several architectures of deep and machine learning were applied to three EEG data segments including resting state, cognitive execution, and a period containing a fusion of those.Main results. The experimental results indicated that EEG-based LSTM networks produced the best performance with an average accuracy of 90.50 ± 0.81% in comparison with the deep neural networks, the convolutional neural networks, and the support vector machines with learning the cognitive state transition of EEG data. Novel observations of individual neural markers showed that the beta power activity of the O1 and O2 sites contributed the most to the classifications, subjects exhibited decreased beta power in the ADHD group, and had larger decreases during cognitive execution.Significance. These findings showed that the proposed EEG-based LSTM networks are capable of extracting the varied temporal characteristics of high-resolution electrophysiological signals to differentiate between ADHD and NT children, and brought a new insight to facilitate the diagnosis of ADHD. The registration numbers of the institutional review boards are 16MMHIS021 and EC1070401-F.

Keywords: ADHD; EEG; continuous performance test; convolutional neural network; deep neural network; long short-term memory network; power spectral density.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Attention Deficit Disorder with Hyperactivity* / diagnosis
Child
Electroencephalography / methods
Humans
Machine Learning
Neural Networks, Computer
Support Vector Machine